Senior MLOps Engineer
London / £75000 - £90000
INFO
£75000 - £90000
LOCATION
London
Permanent
Senior MLOps Engineer
Tech
£75,000 - £90,000
This is an exciting opportunity for an ML Ops & Engineering expert to join a growing data-driven start-up!
THE COMPANY
This very data-driven tech company are continuing to grow their Data presence by hiring an ML Ops Engineer to join their existing Engineering team, working with their Data Science team. It's a relatively new & small team so there's loads of opportunity for growth. You'll be focusing purely on Machine Learning engineering, deployment and building / automating machine learning pipelines so you'll need excellent backend / systems design / software engineering experience but also knowledge of machine learning. It's a great opportunity to work with big data sets and take ownership over MLOps for the business
THE ROLE
As the Senior MLOps Engineer for the business you will:
- Bridge the gap between the Data Science team & Engineering team
- Deploy models into production working with Python
- Maintain the Machine Learning pipeline and make sure everything is running accurately post-deployment
- Build and automate CICD pipelines, ETL pipelines
- Liaise with senior stakeholders across the business
SKILLS AND EXPERIENCE
- Strong academic background - Bachelors / Master's Degree
- Background in Engineering
- Knowledge of Machine Learning models
- Excellent Python coding experience
- Proven commercial experience deploying machine learning models, maintaining ML pipelines and systems design, CICD pipelines
- Knowledge of cloud technologies is essential, preference for AWS
SALARY AND BENEFITS
- Basic salary £75,000 - £90,000 depending on experience
- Benefits
- Bonus
HOW TO APPLY
Please register your interest for this role by sending your CV to Rosie O'Callaghan via the apply link on this page
CONTACT
Rosie O'Callaghan
Business Manager
SIMILAR
JOB RESULTS
Six Top Tips for Hiring MLOps Professionals | Harnham US Recruitment post
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Machine Learning Operations (MLOps) is a discipline that is rapidly growing within the Artificial Intelligence industry. The need for MLOps Engineers has been climbing and is predicted to be one of the most in-demand hires over the next fives years. Unfortunately, it can be extremely difficult to find individuals who are successful in these roles due to a known shortage of experienced professionals. If you’re struggling to find top MLOps talent, here our top tips:Don’t get Caught up on Job Titles Hiring Managers are frequently finding challenges identifying if someone is capable of performing as a high-level MLOps Engineer and this is typical if you base your search on candidates job titles. Since the MLOps field is younger than other, more common, fields in AI there are drastically fewer professionals with the title MLOps Engineer, with this title only emerging in the past couple of years. The key thing to look for are engineers who have proper tooling. For example, a Software or Data Engineer would usually have the needed tooling to easily transition into an MLOps Engineer role, but conceptually do need to understand the nuances of Data Science and Machine Learning. Hiring from the Bottom UpWhen a company starts to build out their MLOps function, it is currently very unlikely that they will be hiring seasoned executives given that the concepts of MLOps are much more recent. This person will much more likely be a hands-on specialist and the founding member of the function. This does not have to be seen as an obstacle, building out an MLOps function will require an individual who wants to work closely with data and, if you already have an executive who understands the value of building out this area, then there is always the option to hire a freelance MLOps Engineer to get things off the ground quickly. Make the Role and Responsibilities Crystal ClearOne of the biggest challenges is figuring out how and where MLOps sits within the organization. One of the obstacles candidates who want to pivot into an MLOps Engineer role face is the lack of clarity in the role and its responsibilities as a direct contribution from the lack of clarity at an organizational level, a problem most experienced with startups. The best way to combat this is to understand exactly how MLOps can benefit the organization and outline the exact needs an MLOps Engineer can satisfy by joining the team. Don’t Assume an MLOps Engineer will stay foreverBuilding out an MLOps function can be extremely difficult if the person who created the infrastructure leaves and this is a major problem employers are facing in the field, particularly with the shortage of MLOps Engineers. It’s critical to make sure everything an MLOps Engineer builds is documented for later use, person dependent and as reproducible as possible. Don’t get Lost in Resumes As important as a good resume can be, for a crucial hire in a premature field what is most important is finding a candidate who has an architectural mindset and excellent tooling. This can be easily achieved if an interview process is tailored to test these two things. A great way to do that is by giving candidates hands-on tasks that are both realistic and simplified.Be Particular When It Comes to AdvertisingIt can be tempting to shy away from putting a safe title on a job description. Many people see it as risky to advertise a role as an MLOps Engineer because it can be intimidating to professionals who want to pivot into MLOps from other titles. Do away with Data Science Engineer, Machine Learning DevOps, and Software Engineer (MLOps). Keep it simple and advertise the role as what it is – an MLOps Engineer role. The candidates you want to attract will not shy away. If you’re looking to build out your Data team or transition into an MLOPs career, Harnham can help. Take a look at our latest MLOps jobs or send me a message to find out more.
Data Engineer Or Software Engineer: What Does Your Business Need? | Harnham US Recruitment post
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We are in a time in which what we do with Data matters. Over the last few years, we have seen a rapid rise in the number of Data Scientists and Machine Learning Engineers as businesses look to find deeper insights and improve their strategies. But, without proper access to the right Data that has been processed and massaged, Data Scientists and Machine Learning Engineers would be unable to do their job properly. So who are the people who work in the background and are responsible to make sure all of this works? The quick answer is Data Engineers!… or is it? In reality, there are two similar, yet different profiles who can help help a company achieve their Data-driven goals. Data Engineers When people think of Data Engineers, they think of people who make Data more accessible to others within an organization. Their responsibility is to make sure the end user of the Data, whether it be an Analyst, Data Scientist, or an executive, can get accurate Data from which the business can make insightful decisions. They are experts when it comes to data modeling, often working with SQL. Frequently, “modern” Data Engineers work with a number of tools including Spark, Kafka, and AWS (or any cloud provider), whilst some newer Databases/Data Warehouses include Mongo DB and Snowflake. Companies are choosing to leverage these technologies and update their stack because it allows Data teams to move at a much faster pace and be able to deliver results to their stakeholders. An enterprise looking for a Data Engineer will need someone to focus more on their Data Warehouse and utilize their strong knowledge of querying information, whilst constantly working to ingest/process Data. Data Engineers also focus more on Data Flow and knowing how each Data sets works in collaboration with one another. Software Engineers – DataSimilar to a Data Engineers, Software Engineers – Data ( who I will refer to as Software Data Engineers in this article) also build out Data Pipelines. These individuals might go by different names like Platform or Infrastructure Engineer. They have to be good with SQL and Data Modeling, working with similar technologies such as Spark, AWS, and Hadoop. What separates Software Data Engineers from Data Engineers is the necessity to look at things from a macro-level. They are responsible for building out the cluster manager and scheduler, the distributed cluster system, and implementing code to make things function faster and more efficiently. Software Data Engineers are also better programers. Frequently, they will work in Python, Java, Scala, and more recently, Golang. They also work with DevOps tools such as Docker, Kubernetes, or some sort of CI/CD tool like Jenkins. These skills are critical as Software Data Engineers are constantly testing and deploying new services to make systems more efficient. This is important to understand, especially when incorporating Data Science and Machine Learning teams. If Data Scientists or Machine Learning Engineers do not have a strong Software Engineers in place to build their platforms, the models they build won’t be fully maximized. They also have to be able to scale out systems as their platform grows in order to handle more Data, while finding ways to make improvements. Software Data Engineers will also be looking to work with Data Scientists and Machine Learning Engineers in order to understand the prerequisites of what is needed to support a Machine Learning model. Which is right for your business? If you are looking for someone who can focus extensively on pulling Data from a Data source or API, before transforming or “massaging” the Data, and then moving it elsewhere, then you are looking for a Data Engineer. Quality Data Engineers will be really good at querying Data and Data Modeling and will also be good at working with Data Warehouses and using visualization tools like Tableau or Looker. If you need someone who can wear multiple hats and build highly scalable and distributed systems, you are looking for a Software Data Engineer. It’s more common to see this role in smaller companies and teams, since Hiring Managers often need someone who can do multiple tasks due to budget constraints and the need for a leaner team. They will also be better coders and have some experience working with DevOps tools. Although they might be able to do more than a Data Engineer, Software Data Engineers may not be as strong when it comes to the nitty gritty parts of Data Engineering, in particular querying Data and working within a Data Warehouse. It is always a challenge knowing which type of job to recruit for. It is not uncommon to see job posts where companies advertise that they are looking for a Data Engineer, but in reality are looking for a Software Data Engineer or Machine Learning Platform Engineer. In order to bring the right candidates to your door, it is crucial to have an understanding of what responsibilities you are looking to be fulfilled.That’s not to say a Data Engineer can’t work with Docker or Kubernetes. Engineers are working in a time where they need to become proficient with multiple tools and be constantly honing their skills to keep up with the competition. However, it is this demand to keep up with the latest tech trends and choices that makes finding the right candidate difficult. Hiring Managers need to identify which skills are essential for the role from the start, and which can be easily picked up on the job. Hiring teams should focus on an individual’s past experience and the projects they have worked on, rather than looking at their previous job titles. If you’re looking to hire a Data Engineer or a Software Data Engineer, or to find a new role in this area, we may be able to help. Take a look at our latest opportunities or get in touch if you have any questions.
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