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Data Engineer

up to £90,000 + Benefits
Manchester (Hybrid)
This is a great opportunity to join a high‑growth adtech scale‑up where you can take ownership of large‑scale data infrastructure that directly powers machine learning, product intelligence, and customer ROI.

THE COMPANY

This is a UK‑based adtech SaaS platform that using machine learning and real‑time behavioural analytics to help brands optimise performance.

THE ROLE

Key responsibilities include:

• Designing, building, and maintaining batch and streaming data ingestion pipelines for high‑volume event data
• Improving and optimising the AWS Redshift data warehouse, including modelling, performance, and cost efficiency
• Refactoring poorly structured data into clean, well‑governed, ML‑friendly datasets
• Building pipelines to support ML workflows, feature stores, A/B testing, and experimentation
• Ingesting and integrating new and under‑utilised data sources
• Working on greenfield projects while scaling existing data infrastructure to billions of data points

YOUR SKILLS AND EXPERIENCE

You will bring strong capability in:

• Python and SQL
• AWS data infrastructure (S3, Redshift, Glue, Athena, Kinesis, Lambda)
• End‑to‑end ownership, from proof‑of‑concept through to production

THE BENEFITS

You will receive a salary of up to £90,000 depending on experience, along with a comprehensive benefits.

HOW TO APPLY
Please register your interest by sending your CV to Molly Bird via the apply link on this page.

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Senior Data Engineer

£70,000-£90,000

London (Hybrid)

This is an exciting opportunity to join a growing tech scale‑up where data is at the heart of their product. You will play a key role in shaping their data infrastructure, enabling large‑scale machine learning and real‑time analytics that directly impact their customers.

THE COMPANY

They are a high‑growth SaaS organisation operating in the digital advertising space. Their platform helps customers optimise performance through advanced analytics, real‑time behavioural insights and machine learning. With a collaborative engineering culture and strong investment backing, they are building scalable data systems to support rapid product expansion.

THE ROLE

As a Data Engineer you will take ownership of the company’s core data pipelines, transforming large‑scale event data into reliable, ML‑ready datasets that power real‑time decisioning and analytics across the business.

Specifically, you can expect to be involved in the following:

• Design and manage data ingestion pipelines that feed into their cloud data warehouse.
• Build and maintain pipelines that prepare data for machine learning and analytics use cases.
• Restructure/improve existing datasets to ensure they are reliable, performant and ML‑ready.
• Work closely with Data Science and Platform Engineering to productionise models and improve data foundations.
• Proven ability to work with high‑volume event data at scale.

SKILLS AND EXPERIENCE

The successful Data Engineer will have the following skills and experience:

• Strong commercial experience in Python, SQL and cloud‑based data engineering.
• Hands‑on experience with AWS services such as Redshift, Athena, S3, Glue and ideally Kinesis.
• Understanding of streaming data, system performance and cost‑efficient architecture.
• A proactive mindset with the confidence to identify and solve problems independently.

BENEFITS

The successful Data Engineer will receive the following benefits:

• Salary between £70,000 – £90,000 – depending on experience.
• Competitive benefits.

HOW TO APPLY

Please register your interest by sending your resume to Majid Latif via the Apply link on this page.

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Senior AI/ML Engineer – Foundation Models & Agentic AI
Life Sciences AI | Biotech / Therapeutics / Computational Biology

What You’ll Work On

• Develop and train proprietary foundation models on multi-omics data (genomics, transcriptomics, proteomics, metabolomics)
• Design novel algorithms to integrate and differentiate across omics modalities
• Own the full training pipeline: data ingestion, tokenization, pretraining, and evaluation
• Push the state of the art – we are building models that do not exist yet

Agentic AI for Life Sciences

• Build and deploy AI agents that assist researchers across therapeutic development, computational chemistry, and biology
• Design multi-agent architectures using LangChain, LangGraph, and custom orchestration layers
• Integrate agents with internal databases, experimental systems, and third-party scientific tools
• Develop agentic workflows for use cases in cosmetics R&D, drug discovery, and clinical analysis

Production ML Infrastructure

• Own model deployment, scaling, and serving infrastructure
• Optimize inference using NVIDIA TensorRT-LLM, Dynamo, Triton, and related tooling
• Partner with software engineering on integration into our broader platform
• Maintain reliability, performance, and observability across deployed models

What We’re Looking For

Required

• Strong hands-on ML/AI engineering experience – you write real code, not just direct others
• Deep proficiency in Python and PyTorch – this is non-negotiable
• Experience training, fine-tuning, and deploying foundation models from scratch or from pretrained checkpoints
• Familiarity with the Hugging Face ecosystem (Transformers, Datasets, PEFT, Accelerate)
• Experience with LangChain and/or LangGraph for agentic pipeline development
• Working knowledge of the NVIDIA stack: TensorRT-LLM, Triton Inference Server, Dynamo
• Comfort with large-scale distributed training tools: DeepSpeed, xFormers
• Strong understanding of state-of-the-art model architectures (transformers, SSMs, diffusion, etc.)
• Ability to collaborate across technical and non-technical teams – bio, chem, software, clinical
• Excellent communication skills – you can explain a training run to a chemist

Strongly Preferred

• Experience with omics data (any modality: genomic, proteomic, transcriptomic, metabolomic)
• Background in computational biology, computational chemistry, or bioinformatics
• Prior work in biotech, pharma, or life sciences AI (not required but a plus)
• Experience building or contributing to multi-agent systems in a production environment
• Track record of independent research or open-source contributions in ML

Technical Skills Summary

Core ML / Training

• PyTorch – primary framework
• DeepSpeed – distributed training and ZeRO optimization
• xFormers – memory-efficient attention and transformer components
• Hugging Face Transformers, PEFT, Accelerate
• Training and fine-tuning large language and multimodal models

Inference & Deployment

• NVIDIA TensorRT-LLM – high-performance LLM inference
• NVIDIA Dynamo – inference orchestration and scheduling
• Triton Inference Server – model serving and batching
• Model quantization, distillation, and latency optimization

Agentic & Orchestration

• LangChain / LangGraph – agent design and multi-agent orchestration
• Tool-use, RAG pipelines, and memory systems
• API and system integration across scientific data sources

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Senior AI Scientist

$180,000-$200,000 base + bonus + benefits

Remote

About the Organization

Join a global financial services company delivering intelligent information and technology solutions to professionals in legal, tax, compliance, and corporate sectors. The team is part of the organization’s innovation hub, focused on applying AI, LLM, and data science to create forward-looking tools.

The environment combines the best of both worlds: startup energy with enterprise support. Projects include building agentic systems to automate tax prep and document summarization for legal and financial workflows.

About the Role

This is a hands-on, model building / development-focused role-ideal for someone with a strong foundation in applied NLP research and a passion for making machine learning work in the real world. You’ll own experiments, iterate on PoCs, and help define how ML models are built for in production environments.

What You’ll Bring

• 5+ years of machine learning and NLP modeling experience in applied research and production environments
• PhD in Computer Science, Computational Linguistics, Statistics or similar highly preferred
• Technical leadership experience required; direct people management a plus
• Experience with LLMs and agents for financial services, legal or compliance
• Background in: NLP: Named Entity Recognition / NER, information extraction, and information retrieval
• Cloud environments (provider-agnostic), AWS or GCP a plus
• Python, PyTorch, TensorFlow, Hugging Face, BERT, RAG experience preferred
• Publication and patent history a plus
• Strong collaboration and communication skills, including experience working with non-technical stakeholders
• Independent problem-solver with a proactive mindset

Preferred Experience

• Technical leadership on NLP products
• Experience delivering LLM-based solutions
• Familiarity with all stages of the AI product lifecycle
• Startup or fast-paced innovation environment experience

HOW TO APPLY

Please register your interest by sending your résumé to Tim Jonas via the Apply link on this page.

KEYWORDS

Machine Learning | GenAI | Gen AI | Generative AI | LLMs | Large Language Models | Artificial Intelligence | Applied Research | Production | AI | Artificial Intelligence | Publications | Patents | PyTorch | Python | Deployment | Hugging Face | RAG | Retrieval Augmented Generation | Agents | Agentic AI | Generative AI | NLP | Natural Language Processing | Chatbots | NER | Information Extraction | Information Retrieval

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Staff Data Scientist, Product

Location: Open / Hybrid / Remote (role-dependent)

Level: Senior Individual Contributor

Role Overview

This role sits within a growing Product Analytics & Data Science team and serves as the technical anchor for experimentation, causal inference, and advanced statistical methods. The Staff Data Scientist will play a critical role in elevating how experiments are designed, executed, interpreted, and trusted across the organization.

Beyond technical execution, this individual will be responsible for shaping experimentation culture, improving analytical rigor, and influencing product and executive stakeholders without direct authority. This is a high-impact IC role for someone who thrives at the intersection of data science depth, product thinking, and organizational influence.

Key Responsibilities

Experimentation & Causal Inference

• Own and evolve the experimentation framework across web and in‑product surfaces
• Design, review, and debug experiments to ensure statistical validity and consistent user experiences
• Apply Bayesian methods, sequential testing, and multi‑arm bandits to real product decisions
• Establish best practices for experimentation design, analysis, and rollout
• Act as the go‑to expert for causal inference questions across the product organization

Product Analytics & Modeling

• Partner with Product, Engineering, and Design to answer complex questions around:
• Feature adoption and usage
• User success and expansion
• Retention and churn drivers
• Build and deploy analytical models that inform product strategy and prioritization
• Translate ambiguous product questions into rigorous analytical approaches with clear recommendations

Technical Leadership & Coaching

• Review analytical approaches and model implementations from other team members
• Coach and mentor data scientists and analysts on advanced statistical techniques
• Set the technical bar for experimentation and analytical quality within the team
• Promote reproducible, well‑documented, and reviewable analytics work

Stakeholder Influence

• Educate non‑technical stakeholders on experimentation, uncertainty, and decision‑making
• Advocate for statistically sound practices in environments where experimentation maturity is low or fragmented
• Build trust in data science outputs through clear storytelling and business‑aligned insights
• Partner with leadership to define and prioritize the experimentation roadmap

Tooling & Infrastructure

• Contribute to the evaluation and implementation of experimentation platforms and tooling
• Work in a collaborative, code‑first environment using version control and peer review
• Ensure analytics and experimentation workflows are scalable and reliable

Required Qualifications

• 6+ years of experience in data science, product analytics, or related fields
• Hands-on expertise in experimentation methodologies, including:
• Bayesian analysis
• Causal inference
• A/B and multivariate testing
• Sequential testing or multi‑arm bandits
• Strong product analytics background within a SaaS or digital product environment
• Proficient in Python and SQL
• Comfortable working in Git-based workflows (PRs, code reviews)
• Experience deploying or productionizing at least one analytical or ML model
• Proven ability to connect technical work to business outcomes

Preferred Qualifications

• Experience evaluating or implementing experimentation tooling
• Exposure to user lifecycle analytics (activation, adoption, retention, churn)
• Familiarity with modern analytics engineering or experiment platforms
• Comfort working in ambiguous environments with evolving data maturity

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Senior Computer Vision Engineer (2D or 3D – Clinical AI)

About the Company

The mission is simple but ambitious: democratize access to high?quality cardiac care, especially in regions where cardiologists and advanced imaging equipment are scarce.

About the Role

This is a hands-on applied engineering position focused on building and deploying real clinical AI, not academic experiments. You will own production imaging models end?to?end, ensure reliability across diverse clinical settings, and work closely with clinical, regulatory, and deployment teams.

Profile A –

• Segmentation
• Localization
• Ultrasound/Echo image processing
• Signal and noise?robust imaging pipelines

Profile B – 3D Modeling

• Volumetric reconstruction
• 3D geometry and physics-informed modeling
• Computational representations of cardiac structures

Exceptional candidates may be considered for both tracks.

What You Will Own

Computer Vision Development

• Build production-grade CV models used in live cardiac workflows
• Develop 2D segmentation, 3D/4D reconstruction, and mathematical modeling systems
• Optimize models for low?latency clinical deployment
• Debug imaging pipelines across noisy, highly variable datasets

Production ML & Deployment

• Own pipelines from prototype ? production
• Support deployment engineers with real?world constraints
• Maintain model reliability across multiple international regions
• Monitor drift, performance degradation, and operational issues

What We’re Looking For

Required

• Strong experience in Computer Vision (2D and/or 3D)
• Production ML experience-beyond research or academic prototypes
• Background in biomedical or clinical imaging (ultrasound strongly preferred)
• Ability to debug, iterate, and deliver under pressure
• Experience working with deployment or ops engineering teams
• Comfort writing regulatory?oriented technical documentation
• Strong Python + PyTorch skills
• Startup mindset – bias for action, adaptability, ownership

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Global Network Director

Role Overview

We are seeking a hands-on, high-impact Global Network Director to lead, align, and deliver across a large, distributed global organization.

This leader will operate with a builder mindset – architecting, modernizing, and scaling a global network ecosystem that supports enterprise offices, hyperscale data centers, and private cloud infrastructure.

The role requires a rare combination of deep technical credibility, strategic alignment with executive leadership, and the ability to execute at speed. This is not a maintenance role – it is a transformation mandate.

Scope of Responsibility

Oversight includes:

• Enterprise administration offices
• Global sales offices
• Design offices
• Enterprise data centers
• Hyperscale data center environments
• Private cloud infrastructure with AWS and Azure footprint

You will lead approximately 40-55 professionals across global teams, including internal engineers, support functions, and managed service partners.

Budget ownership and operational accountability are expected at scale.

Core Mandate

• Lead and deliver a global network transformation
• Treat Network as a Product / Network as a Service
• Drive an automation-first infrastructure strategy
• Align network strategy tightly with enterprise vision and business objectives
• Build and evolve teams end-to-end (hire, mentor, scale, optimize)
• Execute both strategic roadmap creation and hands-on architectural oversight

Technical Requirements

Network Architecture

• Deep expertise in spine-leaf architecture (strong preference)
• Experience with Arista or Cisco (leading or participating in Cisco-to-Arista transformation preferred)
• Design and build enterprise and hyperscale data centers from scratch
• Modern network design principles and scalable architectures

Network Engineering & Automation

• Network engineering leadership at enterprise scale
• Strong automation mindset (Terraform / DevOps frameworks)
• Infrastructure-as-Code expertise
• SRE / NRE operational philosophy
• VPN technologies

Network Security

Hands-on experience with:

• Palo Alto
• Fortinet
• CIS Controller or similar security governance platforms

Security architecture must be integrated into network design from day one.

Data Center & Cloud Strategy

• Build and operate modern private cloud infrastructure
• Maintain strong AWS and Azure footprint
• Cloud-agnostic mindset with modern cloud-native mentality
• Lead investments in private data centers as GPU costs and scale requirements increase
• Balance on-prem infrastructure with evolving Software-as-a-Service models

Ideal Background

• Experience in high-growth technology environments preferred
• Proven track record delivering large-scale network and data center transformations
• Experience treating the network as a product rather than a utility
• Strong execution history – not just strategic oversight
• Experience managing significant budgets and large global teams

Candidates from highly traditional environments (e.g., purely finance or healthcare legacy infrastructures) may be less aligned with the transformation mandate.

Leadership Profile

• Builder mentality – energized by creating from scratch
• High energy, delivery-oriented, and hands-on
• Capable of learning, teaching, and scaling teams A-Z
• Strong alignment with executive leadership on strategy and execution
• Long-term ownership mindset

What Success Looks Like

• Fully modernized, automation-first global network
• Successful Cisco-to-Arista transformation
• Scalable private cloud and hyperscale data center capability
• Network operating as a product with strong internal service delivery
• High-performing global team aligned to strategy and execution

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Director of Cloud & Private Cloud Engineering

Overview

This role leads a large‑scale Cloud and Private Cloud Engineering organisation within a highly technical, engineering‑driven enterprise environment. The Director owns both private and public cloud platforms and is responsible for unifying previously siloed infrastructure efforts into a single, coherent cloud platform that supports enterprise and product workloads at scale.

The organisation is building much of its cloud platform from the ground up using modern, open‑source tooling. While strong technical capabilities already exist across engineering teams, this role exists to provide the strategic ownership, connective leadership, and execution focus needed to bring the platform together and drive real delivery.

The Organisation & Scope

• Lead an organisation of 50-100 engineers, including senior managers and directors
• Teams span SRE, DevOps, platform engineering, and cloud infrastructure
• Geographic footprint primarily across the US and India
• Responsible for both private cloud and public cloud environments

Role Responsibilities

• Own the end‑to‑end strategy and execution for private and public cloud platforms
• Unify fragmented cloud efforts into a single, integrated platform
• Drive execution – moving strategy and architecture into delivered, production‑ready systems
• Provide directional leadership to architects and senior engineers without acting as the primary architect
• Build trust, autonomy, and accountability across a large, established engineering organisation
• Oversee compute, GPU, storage, Kubernetes, and cloud infrastructure
• Maintain visibility into cost, reliability, scalability, and performance
• Partner across engineering, security, and enterprise IT stakeholders to enable adoption

Why This Role Exists

The organisation has strong technical depth, but cloud capabilities are currently developed in pockets. Public cloud usage is progressing well, Kubernetes is underway but not yet standardized, and private cloud and public cloud remain disconnected.

This role exists to join the dots, establish unified ownership, and ensure that cloud strategy is not just designed-but delivered. The team needs a leader who can push architectural thinking in the right direction, remove blockers, and execute at scale.

Required Experience & Profile

• 12+ years in cloud, private cloud, infrastructure, or platform engineering
• 5+ years leading managers and senior technical leaders
• Demonstrated experience across both private and public cloud (core requirement)
• Deep private cloud expertise plus strong public cloud experience across at least two of:
  • AWS
  • GCP
  • Azure
• Strong Infrastructure‑as‑Code background (Terraform, Ansible, Python, or equivalent)
• Proven experience building self‑service internal platforms or IaaS products
• Strong foundation in networking, security, identity, and distributed systems
• Track record of earning trust and leading large, senior engineering organisations
• Known as an executor – able to drive strategy into delivery

Nice to Have

• Leadership experience with OpenShift or upstream Kubernetes
• Experience delivering portal‑ or GUI‑based private cloud platforms
• Background in DevOps, SRE, or Internal Developer Platforms (IDPs)
• Exposure to AI/ML or GPU‑driven workloads

Ideal Background

This role is best suited to candidates from stable, established technology organisations, particularly environments where private cloud and public cloud have coexisted over time. Familiarity with enterprises that use “private cloud” or “hybrid/cloud platform” terminology and architectures will be beneficial. Strong open‑source fluency and comfort operating in large, complex engineering environments are key to success.

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Role Overview

This is a hands‑on leadership role responsible for building a modern cloud engineering function from the ground up. The environment currently includes AWS and GCP, but lacks structure, governance, best practices, and clear ownership. You will be the first dedicated public cloud engineering leader, responsible for assessing the current state, defining strategy, establishing standards, and building a scalable cloud foundation.

The role blends deep technical expertise, team leadership, and cross‑functional partnership, especially with the private cloud organization. The initial phase involves auditing and stabilizing the existing cloud estate; the next phase focuses on building seamless connectivity between public and private cloud environments.

Key Responsibilities

1. Cloud Strategy & Architecture

• Audit the current AWS and GCP environments to understand existing workloads, configurations, and gaps.
• Define a cohesive multi‑cloud strategy with strong governance, security, identity, and operational standards.
• Establish best‑practice architecture for scalable, resilient, cost‑efficient cloud platforms.

2. Hands-On Engineering & Infrastructure Build

• Lead Infrastructure as Code adoption using Terraform (highest priority), plus Ansible and Python.
• Build core cloud foundations: networking, IAM, monitoring, automation, observability, and cost controls.
• Modernize cloud environments to enable workload mobility between public and private platforms.
• Own cloud reliability, incident response, security posture, and FinOps optimization.

3. Leadership & Team Building

• Lead and grow the cloud engineering team; initially operate as a hands-on technical manager.
• Hire, mentor, and develop engineers with strong technical depth.
• Serve as the senior cloud architecture expert for internal stakeholders.

4. Cross‑Functional Partnership

• Partner closely with the private cloud organization to design hybrid connectivity models.
• Align public and private cloud roadmaps, access patterns, networking design, and cost allocation.
• Work with legacy infrastructure teams to standardize architecture and modernize existing systems.

What You’ll Be Doing Day‑to‑Day

• Auditing and restructuring AWS and GCP environments
• Implementing governance, access policies, and cloud operating standards
• Writing Terraform modules and IaC patterns
• Designing hybrid architecture to enable smooth workload mobility
• Managing reliability, incident response, and cloud cost optimization
• Leading cloud engineering hiring and team development
• Acting as the SME for AWS, GCP, and modern cloud patterns

Required Experience

• 8+ years in cloud, infrastructure, platform, or DevOps engineering
• 3+ years leading teams (formal or informal leadership acceptable)
• Deep expertise in AWS (priority) and strong capability in GCP or another public cloud
• Strong experience building cloud environments from scratch-not just maintaining inherited ones
• Expert-level proficiency with Terraform
• Strong understanding of cloud networking, identity, security, distributed systems, and hybrid architectures
• Demonstrated experience designing and operating hybrid/public-private cloud ecosystems

Preferred Qualifications

• AWS Solutions Architect Professional certification (strong signal for this hiring profile)
• Kubernetes, Docker, EKS, GKE
• DevOps or SRE background
• Experience with GPU, AI/ML, or high-performance compute workloads
• Experience in highly scaled enterprise or regulated environments

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Senior Analytics Engineer

Overview

A rapidly growing consumer products company is seeking a Senior Analytics Engineer to help build and scale a modern data platform. This role sits at the intersection of analytics engineering, data infrastructure, and business intelligence, enabling teams across the organization to make data-driven decisions.

The company operates a U.S.-based manufacturing environment and a strong direct-to-consumer ecommerce platform. As the organization continues to scale, the data function is being built from the ground up, creating an opportunity for a hands-on engineer to shape the architecture, pipelines, and analytics capabilities of the business.

Responsibilities

Data Platform Development

• Build, maintain, and optimize data models using SQL and DBT
• Support migration and development of a centralized data warehouse environment
• Design scalable data architecture and transformation layers
• Improve reliability, performance, and maintainability of analytics infrastructure

Data Pipeline Engineering

• Develop and maintain ETL/ELT pipelines using modern data tools
• Expand and optimize ingestion pipelines from operational systems
• Write custom workflows and integrations using Python
• Ensure data quality, monitoring, and pipeline stability

Business Intelligence & Analytics

• Develop and maintain dashboards and reporting solutions
• Enable self-service analytics for business teams
• Work directly with stakeholders to translate business needs into data solutions
• Support analytics across key functions including:
• Supply chain
• Ecommerce performance
• Marketing analytics
• Sales performance
• Forecasting and operations

Data Governance & Reliability

• Establish trusted datasets and consistent data definitions
• Improve data documentation and discoverability
• Troubleshoot data issues and analytics requests across teams
• Ensure long-term scalability of the analytics ecosystem

Required Qualifications

• 4+ years of experience working with SQL
• 4+ years of experience using DBT
• 4+ years of experience building dashboards and BI solutions
• Experience building and managing data pipelines and ETL workflows
• Strong understanding of data warehousing concepts
• Ability to work independently in a fast-paced, evolving environment
• Strong communication skills and experience collaborating with non-technical stakeholders

Preferred Qualifications

• Experience working with BigQuery
• Experience building dashboards in Looker
• Python for data workflows or ingestion pipelines
• Experience with ecommerce analytics
• Experience analyzing Shopify or similar commerce platforms
• Experience working with manufacturing or supply chain data

Ideal Candidate Background

Strong candidates often come from:

• Ecommerce organizations
• Manufacturing companies
• Businesses operating direct-to-consumer sales models
• Mid-sized companies where individuals have broad ownership of the data stack

Experience analyzing

• Ecommerce sales performance
• Supply chain operations
• Marketing attribution
• Product and operational data

Work Environment

• Hybrid work model with 2-3 days per week in office
• Collaboration with a small technical team including IT and data science
• Fast-paced environment with significant opportunity to influence the company’s data strategy
• High level of autonomy and ownership over technical solutions

What We’re Looking For

• Curious and evidence-driven
• Comfortable working with ambiguity
• Self-directed and proactive
• Passionate about learning new technologies
• A strong problem solver who enjoys building scalable systems

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