DATA & AI STAFFING
SAN FRANCISCO

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Principal AI Engineer

New York, New York – hybrid

$225,000 – $250,000 + bonus + LTI; $500,000-$700,000 total

THE COMPANY

Harnham is partnering with one of the most well known financial services companies, which is looking for an experienced AI / ML Engineer. This person will be at the forefront of building AI automation applications for new ways at identifying cost-saving opportunities. You’ll partner with executive teams across the company and own generative AI, LLM and reinforcement learning modeling and deployment.

RESPONSIBILITIES

• Work closely with department heads to align on business goals, define machine learning challenges, and design effective ML solutions.

• Develop and deploy machine learning models, including building data pipelines, orchestrating ML workflows, and optimizing system performance and reliability.

• Maintain a robust codebase by writing well-tested code, covering both functional and non-functional aspects such as unit, integration, and load testing.

• Stay informed on industry trends and advancements in generative AI, LLMs, agentic AI, deep learning, experiment with new model concepts, and run both offline and online evaluations.

• Actively share knowledge through internal presentations, tech talks, and by promoting best practices in engineering and technology use.

SKILLS AND EXPERIENCE

• Advanced degree (Master’s or PhD) in Computer Science, Machine Learning or a related field.

• Over 7 years of hands-on experience developing and deploying production-ready machine learning / deep learning / AI systems, covering the full model lifecycle-training, tuning, deployment, serving, and monitoring.

• Enterprise-level application experience, ideally in real-time preferred.

• Expertise in cloud infrastructure (especially AWS), machine learning orchestration tools like Kubeflow, TensorFlow, and the use of Feature Stores in live environments.

• Experience as a tech-lead for scaling MLE teams preferred.

• Commercial experience combination of big tech and scaling startups / scrappy environments a plus.

BENEFITS

The compensation package contains a base salary, bonus, LTI and a comprehensive benefits package.

HOW TO APPLY

Please register your interest by sending your CV via the Apply link on this page.

KEY TERMS

Artificial Intelligence | Generative AI | GenAI | Machine Learning | ML Engineer | Engineering | Deployment | Production | Real Time | Enterprise | Statistics | Mathematics | Financial Services | Banking | Python | Anomaly Detection | Fintech | Agentic AI | AI Agents

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VP AI Engineer
$220K base + bonus + RSU
New York or San Francisco

A global financial technology organisation operating at enterprise scale, combining advanced software engineering, data science, and applied AI to modernise large-scale production systems. The company invests heavily in automation, operational excellence, and AI-driven infrastructure, building mission-critical platforms used across complex, high-reliability environments.

Mission

• Design and deploy agentic AI systems to automate and optimise large-scale production environments
• Build LLM-powered solutions that diagnose issues, reason over complex systems, and take secure, auditable actions
• Develop RAG pipelines and domain knowledge systems with strong data quality, feedback loops, and governance
• Productionise LLMs: evaluation frameworks, prompt orchestration, response validation, self-correction loops
• Integrate AI agents with observability, incident management, deployment, and runtime platforms
• Implement safety, reliability, and compliance guardrails (policy enforcement, rollback strategies, circuit breakers)
• Optimise performance, cost, and latency through prompt engineering, caching, routing, batching, and streaming

Profile

• Bachelor’s degree in a quantitative or computational field (Master’s / PhD preferred)
• 7+ years of experience in applied ML, data science, or software engineering in production environments
• Strong hands-on development in Python (or C++ / Java / Go), building large-scale applications
• Practical expertise with LLMs: prompt engineering, fine-tuning/adaptation, RAG, tool-calling agents, vector search
• Experience building secure, explainable, and reliable AI systems with strong governance and auditability
• Comfortable collaborating across engineering, infrastructure, and operations teams with measurable business outcomes

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Job Opportunity: Principal AI Engineer | Remote or Hybrid

Location: Remote or Hybrid (HQ in [City])

Role Type: Full-Time

Seniority: Principal / Expert

Start Date: ASAP

About the Company

The company is expanding its AI capabilities to build innovative, intelligent systems that power next-generation products and experiences. With an ambitious roadmap centered on generative AI, advanced machine learning, and large-scale automation, they are seeking a highly experienced Principal AI Engineer to lead technical strategy, guide system architecture, and drive innovation across the organization.

This is a pivotal role that will shape the long-term AI direction, influence key engineering decisions, and deliver solutions that have significant impact on the company’s technical evolution.

The Role

The Principal AI Engineer will act as a hands-on technical leader, responsible for designing, developing, and scaling advanced AI systems. The role involves high-level architectural thinking, applied research, and deep technical execution, including oversight of model development, evaluation, deployment, and optimization.

The successful candidate will collaborate with engineering, data, and product stakeholders to define the AI vision while also mentoring teams and setting engineering standards that enable excellence and innovation.

Key Responsibilities

• Lead the design and development of complex AI systems, including generative AI models, LLM-powered applications, and large-scale machine learning infrastructures.
• Define and drive the company’s AI technical strategy, ensuring alignment with business goals.
• Oversee model experimentation, fine-tuning, evaluation, and deployment into production environments.
• Set best practices for ML engineering, model lifecycle management, security, and governance.
• Provide expert guidance on architecture decisions across data, ML, and application layers.
• Collaborate with cross-functional teams to identify high-impact AI opportunities and translate them into technical solutions.
• Mentor and support engineering teams, elevating technical capabilities across the organization.
• Stay ahead of industry developments, research breakthroughs, and emerging AI frameworks.

Required Experience

• 7+ years of experience in AI/ML engineering, with a strong track record of delivering production-grade AI systems.
• Deep expertise in Python and modern ML frameworks (PyTorch, TensorFlow, JAX).
• Extensive hands-on experience working with large language models, generative AI architectures, and related tooling.
• Strong knowledge of model serving, orchestration, distributed training, and scalable inference systems.
• Experience architecting and deploying AI solutions on cloud platforms (AWS/GCP/Azure).
• Proven ability to lead complex AI initiatives, influence technical direction, and guide multidisciplinary teams.
• Strong understanding of data engineering, MLOps, model governance, and ML lifecycle best practices.
• Ability to thrive in a fast-paced environment and navigate ambiguous, greenfield challenges.

What the Company Offers

• A strategic role with significant influence over the AI roadmap and technical foundations.
• Competitive salary, equity, and comprehensive benefits.
• Flexible remote or hybrid working environment.
• Opportunities to work with state-of-the-art AI technologies and contribute to groundbreaking products.
• A culture that values innovation, autonomy, and continuous learning.

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Senior AI Engineer – 2 days Manchester

You will be responsible for designing and delivering production-ready AI and ML solutions that drive business value. This role focuses on building scalable data pipelines, deploying machine learning models, and embedding AI into products and decision-making.

Responsibilities

• Build and maintain end-to-end AI/ML pipelines
• Develop, deploy and monitor machine learning models
• Apply MLOps best practices including CI/CD
• Collaborate with data, analytics and engineering teams
• Ensure data governance, security and responsible AI

Experience

• Strong experience in ML engineering and data pipelines
• Python, SQL and Git proficiency
• Hands-on use of ML frameworks (e.g. TensorFlow, PyTorch, scikit-learn)

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Lead & Principal AI Engineer – Remote

A global B2B technology organisation is expanding its AI engineering function. After several strategic acquisitions, it now offers a broadened suite of digital services and operates with a remote-first model across Europe.

What You’ll Work On
* Building AI platforms that power conversational experiences and intelligent purchase flows
* Designing and implementing backend services and tooling for AI-driven features
* Enabling internal engineering teams to adopt AI effectively
* Developing scalable, secure, and high-quality systems using strong software engineering practices

Required Background
* Master’s degree in Computer Science or a related field
* Expertise in Python, Node.js, and AWS
* Strong foundation in backend or platform engineering

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AI Engineer – Remote

We are currently working on an AI Engineer role with a global language and translation company. You will be designing, developing, and deploying cutting-edge machine learning solutions across the company.

If you enjoy end-to-end ownership (from experimentation to deployment), working with AWS, Docker, MLflow, TensorFlow/PyTorch, and contributing to innovative projects with a flexible, results-focused culture, this could be an exciting match.

Skills:

• Ability to write and produce production-grade code in Python
• Experience with TensorFlow, PyTorch and Scikit-learn
• Experience with NLPs and LLMs
• Speech, Text or Audio data
• Strong knowledge of machine learning techniques and algorithms, including supervised and unsupervised learning, deep learning, and reinforcement learning

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