Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

ML Ops Engineer

Salt
London
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Operations Lead

Machine Learning Engineer

Machine Learning Operations Engineer

Machine Learning Operations Engineer

Senior Data Scientist

Senior Data Scientist

Senior Machine Learning Ops Consultant – Banking Client – Brussels

Rate: €600 – €900pd

Duration: 1 year contract

Hybrid working: You will be needed onsite in Brussels for team meetings and workshops

We are currently looking for an experienced ML Ops Consultant to join the team on a freelance contract.

As a competency centre for AI/ML, the team helps improve process efficiency and generate insights using techniques such as predictive modelling, natural language processing, GenAI and mathematical optimization.

Qualifications

You have a proven track record of hands-on experience in the area of AI/ML/Advanced Analytics, with special focus on deploying and maintaining AI/ML models and services in production. Keywords: AI/ML application development, testing, serving, monitoring, troubleshooting. You know how to ensure ML models are reproducible and interpretable. You have already single-handedly packaged and deployed AI/ML services to production. You know how to monitor and maintain AI/ML services post-deployment. You are proficient in Python You have 5+ years of work experience with Python, and AI/ML standard libraries such as pandas, scikit-learn, xgboost Nice-to-haves: Data processing libraries and frameworks (pydantic, pandera) Web frameworks (such as FastAPI, Flask, …) CLI frameworks (Typer, Click, …) General MLOps tools and frameworks (MLFlow, Azure ML Studio, …) Version control tools for ML datasets and models (DVC, Azure ML Dataset, …) Monitoring libraries and solutions (such as NannyML, Evidently AI, …) Distributed processing libraries and frameworks (such as Ray, Dask, PySpark, …) Pipeline-building and orchestration libraries (such as Metaflow, ZenML, Kedro, Airflow, Dagster, …) General Python development tool (pytest, coverage, tox, mypy, black, ruff, uv, pip-compile, …) You can write both object-oriented and functional code, and understand concepts such as (de)coupling, coherence, inheritance, composition. You make sure the code that you and your colleagues write is thoroughly tested (unit, integration, end-to-end, stress/performance). You love and regularly use data validation and type hints. You know how to turn a messy jupyter notebook into a production-grade piece of code. Although we’ll apply all possible preventive measure to prevent this from ever happening. You know how to package a python application or library for distribution You are a proficient GIT user, able to collaborate with multiple developers on multiple repositories, while following best practices related to branching, merging and code reviews. You have a good understanding of Machine Learning algorithms and their applications in NLP. You have work experience with at least one Cloud Provider, preferably Azure Cloud. You have experience with Unix/Linux command line tools and scripting (shell, bash): VIP club membership if you have at least once ran `rm -rf` on production data. You possess the foundational Data Engineering skills, allowing you to interact with the Data Engineering team, and analyze and troubleshoot data pipelines if needed: You could handle using SQL to extract, transform and load data (ETL/ELT). Experience with the Hadoop ecosystem (Spark, Kafka, Hive, Impala…) is a plus. Experience with the Cloudera distribution is an additional plus You understand the modern MLOps framework and complexities it adds to DevOps. You are able to identify the MLOps maturity gaps and provide inputs for modernization efforts.

Non-technical

You have strong verbal and written communication skills as well as good customer relationship skills to present complex concepts and/or the results of a use case to different audiences (from end users up to division management). You have experience of working in large, complex enterprises and have stoically accepted it as your fate. You are not allergic to legacy technology, yet are always on the lookout for modernization opportunities. You stay up-to-date with new tools, technologies and approaches within the domain. You are a well-integrated team player. You are able to estimate your short-term effort with reasonable accuracy and get the work done in the time frame you commit to. You successfully swim in the waters of Agile project management techniques (scrum boards, standups, demos, reviews). You stand to promote MLOps and advocate for its usage and necessity across the organization. Must love mentoring and sharing knowledge. Must love dad jokes.

Candidates must be based in either Belgium, France, The Netherlands or the UK (IR35 check needed).

Your formal qualifications are the following:

University degree in software engineering OR Data Science/Machine Learning/Data Engineering OR a related quantitative field, combined with strong IT skills. 5+ years of experience with Python 2+ years of experience of using DevOps/CI/CD practices. 2+ years of experience in deploying AI solutions to production.

Please do send across an up to date CV to

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Write an AI CV that Beats ATS (UK examples)

Writing an AI CV for the UK market is about clarity, credibility, and alignment. Recruiters spend seconds scanning the top third of your CV, while Applicant Tracking Systems (ATS) check for relevant skills & recent impact. Your goal is to make both happy without gimmicks: plain structure, sharp evidence, and links that prove you can ship to production. This guide shows you exactly how to do that. You’ll get a clean CV anatomy, a phrase bank for measurable bullets, GitHub & portfolio tips, and three copy-ready UK examples (junior, mid, research). Paste the structure, replace the details, and tailor to each job ad.

AI Recruitment Trends 2025 (UK): What Job Seekers Must Know About Today’s Hiring Process

Summary: UK AI hiring has shifted from titles & puzzle rounds to skills, portfolios, evals, safety, governance & measurable business impact. This guide explains what’s changed, what to expect in interviews, and how to prepare—especially for LLM application, MLOps/platform, data science, AI product & safety roles. Who this is for: AI/ML engineers, LLM engineers, data scientists, MLOps/platform engineers, AI product managers, applied researchers & safety/governance specialists targeting roles in the UK.

Why AI Careers in the UK Are Becoming More Multidisciplinary

Artificial intelligence is no longer a single-discipline pursuit. In the UK, employers increasingly want talent that can code and communicate, model and manage risk, experiment and empathise. That shift is reshaping job descriptions, training pathways & career progression. AI is touching regulated sectors, sensitive user journeys & public services — so the work now sits at the crossroads of computer science, law, ethics, psychology, linguistics & design. This isn’t a buzzword-driven change. It’s happening because real systems are deployed in the wild where people have rights, needs, habits & constraints. As models move from lab demos to products that diagnose, advise, detect fraud, personalise education or generate media, teams must align performance with accountability, safety & usability. The UK’s maturing AI ecosystem — from startups to FTSE 100s, consultancies, the public sector & universities — is responding by hiring multidisciplinary teams who can anticipate social impact as confidently as they ship features. Below, we unpack the forces behind this change, spotlight five disciplines now fused with AI roles, show what it means for UK job-seekers & employers, and map practical steps to future-proof your CV.