Making machine learning work in production.
Every prediction a customer sees, whether a fraud check, a recommendation or a price, depends on a model someone trained, deployed and kept running. MLOps and machine learning engineers make that possible. As UK organisations move from AI pilots to products people rely on, engineers who can operate models reliably at scale have become central to whether machine learning delivers on its promise.
We work with start-ups shipping their first models, scale-ups building platforms that keep pace with growth, and fintechs running machine learning where accuracy, explainability and resilience are non-negotiable, introducing engineers to teams where production really matters.
Latest roles
London, United Kingdom
£50,000 - £70,000
Posted 2 months ago
Kent, United Kingdom
£40,000 - £70,000
Posted 1 month ago
Establishing your MLOPs career
MLOps and machine learning engineering are production engineering disciplines, so many people move in from neighbouring roles:
- Software engineering: bringing version control, testing and deployment experience, and adding machine learning tooling
- Data engineering: bringing pipelines and infrastructure experience, and adding model serving and monitoring
- Data science: bringing modelling expertise, and adding the engineering needed to run models reliably
- Graduate schemes and apprenticeships: combining structured learning with hands-on experience
Python, a cloud platform such as Azure, AWS or Google Cloud, and Docker form a practical foundation, even at junior level. Production experience is the hardest part to show before your first role, so a small end-to-end project makes a real difference. A simple model served behind an API, containerised, with basic logging and monitoring, shows employers you understand what happens after training. Being able to explain what went wrong along the way, and how you fixed it, often makes the strongest impression.
There’s no single required qualification for machine learning engineering, but a few routes are common:
- Degrees: computer science, maths, statistics, engineering, physics or a related subject, which give a strong grounding in programming and the maths behind machine learning
- Postgraduate study: a master’s in machine learning, AI or data science can help, particularly if your first degree is in a different field
- PhDs: most valuable for research-focused or specialist roles close to model design, but not usually required for MLOps or applied engineering positions
- Apprenticeships and graduate schemes: combining paid work with structured learning in software, data or machine learning
- Cloud certifications: machine learning and AI certifications from Microsoft, AWS or Google can add structure and show familiarity with production platforms
Qualifications help open doors, but practical evidence usually makes the strongest case. A project that deploys and monitors a model, or experience taking machine learning into production in a software or data role, shows employers you can do the job, whatever your academic background.
MLOps and machine learning engineering combine machine learning knowledge with production engineering. These skills matter most early on:
- Python: the standard language across model training, pipelines and tooling
- Machine learning fundamentals: how models are trained, evaluated and improved, using frameworks such as PyTorch
- Docker and containerisation: packaging models so they run consistently anywhere
- Cloud platforms: a working knowledge of Azure, AWS or Google Cloud
- CI/CD: automating how models are tested and deployed
- Experiment tracking and versioning: tools such as MLflow for managing models and their history
- Monitoring: checking whether a model still performs once it’s live
- Software engineering practices: version control, testing and code review
As your career develops, orchestration platforms, system design, large language model deployment and model optimisation become increasingly valuable. Early on, curiosity about why a model’s performance changes, and how to investigate it, is a real strength.
Machine learning engineering is among the better-rewarded areas of UK technology, and pay grows with experience and scope. Many engineers start in software, data or graduate roles before moving into dedicated ML positions. As engineers take on production platforms, large language model deployment, system design and technical leadership, earning potential increases. Scope and seniority usually shape pay more than whether a role is titled ML Engineer or MLOps Engineer.
| Seniority Level | Typical Permanent Salary |
| Junior ML / MLOps Engineer (0-2 years) | £40,000 - £58,000 |
| ML / MLOps Engineer / Mid-level (2-5 years) | £58,000 - £85,000 |
| Senior ML / MLOps Engineer | £85,000 - £115,000 |
| Lead / Principal ML Engineer |
£110,000 - £145,000 |
| Head of MLOps / Head of Machine Learning |
£130,000 - £170,000+ |
Contract day rates typically run £450 to £850+, sitting close to £600 at the median, noticeably higher than general data engineering contracts and reflecting how few candidates can genuinely do this work end to end.
London commands the clearest premium in this specialism of anywhere we recruit - up to £160,000, against roughly £45,000 in the regions - though genuinely senior MLOps talent is scarce enough everywhere that strong remote candidates rarely stay on the market long, wherever they're based.
| Seniority Level | Average Permanent Salary |
| London | £65,000 - £160,000 |
| South-East | £58,000- £120,000 |
| Manchester & North-West | £48,000- £100,000 |
| Leeds, Birmingham & regional cities |
£45,000- £90,000 |
| Remote (UK-based) | £50,000- £110,000 |
Moving your AI Career forward
Experienced engineers often find their careers splitting between platform and infrastructure on one side, and modelling and research on the other. Several directions are open:
- Lead or Principal ML Engineer: setting technical direction for models and platforms, and guiding architecture decisions others build on
- ML Platform or MLOps Lead: owning the shared infrastructure, tooling and reliability that many teams depend on
- ML Architect: designing how machine learning systems, data platforms and existing technology fit together
- Applied or research-focused roles: going deeper into model design and advanced techniques
- Head of MLOps or Head of Machine Learning: leading teams and owning the machine learning roadmap
At senior level, system design, reliability and incident response often matter as much as model performance. Taking ownership of deployment pipelines, monitoring and post-incident reviews builds the experience lead roles call for, and mentoring others through their first production issues is a valuable leadership signal. We can help you understand where your experience is most valued.
MLOps engineers sit between data and software engineering, and our 2026 Big Tech Employment Survey suggests professionals in those two areas see AI’s impact differently. That reflects the mixed picture for MLOps itself. AI tools now help with routine work such as deployment scripts, configuration and first-draft code, which shifts the emphasis towards judgement: deciding what should be automated, designing platforms that fail safely, and knowing when a model’s behaviour needs human attention.
AI is also expanding the work. Generative AI and agentic systems bring new challenges, from serving large models efficiently to evaluating outputs that aren’t always predictable and governing how AI is used. In banking, fintech and other regulated environments, that governance work carries particular weight.
Our view is that AI is raising the importance of the platform decisions experienced engineers make. Those who combine strong production foundations with practical generative AI experience are well placed for the roles emerging around it.
Machine learning engineering roles span a wide range of organisations, and the maturity of the work varies considerably:
- Start-ups building from 0-1: taking a first model into a customer-facing product, often building the platform from scratch
- Scale-ups after product-market fit: automating retraining, controlling costs and scaling models as usage grows
- Banks, fintechs, insurers and investment firms: fraud, credit risk and trading models running at production scale, with strong governance and explainability
- Technology and AI-led businesses: embedding machine learning and generative AI directly into products
- Public sector and third sector: operational and policy modelling, sometimes requiring security clearance
- Consultancies: delivering ML platforms and solutions across multiple clients
Some of the most rewarding roles sit with organisations that have proven a model’s value and are now focused on running it reliably in production. A clearly defined platform challenge, with investment behind it, is often a good sign of a mature machine learning team. We can help you understand how teams differ in maturity and scope.
Both careers focus on making machine learning work in the real world, but they suit different strengths. Machine learning engineers concentrate on the model itself: building, training, evaluating and taking it into production. MLOps engineers concentrate on the platform around it: deployment pipelines, infrastructure, monitoring and keeping many models running reliably at scale.
Which suits you depends on the work you enjoy most:
- ML engineering: suits engineers who enjoy experimentation, improving model performance and working closely with data scientists on what a model should do
- MLOps: suits engineers who enjoy automation, reliability and building platforms that other teams depend on
It’s also worth considering where you want to work. In start-ups and smaller teams, the roles often combine, so breadth across both is valuable. Larger organisations are more likely to separate them, which can offer deeper specialisation in either direction. Skills transfer well between the two, and many experienced engineers move between them over time. Scope and seniority usually shape your career more than the title itself.
LLMOps applies MLOps principles to large language models and generative AI systems. For experienced ML and MLOps engineers, it builds naturally on skills you already have, while adding some new challenges:
- Model serving and optimisation: running large models efficiently, balancing latency, cost and scale
- Retrieval and orchestration: connecting models to data and tools, using frameworks such as LangChain or LangGraph
- Evaluation: measuring quality and reliability when outputs aren’t always predictable
- Guardrails and safety: controlling how models behave and what they can access
- Monitoring and cost control: tracking usage, performance and spend in production
- Governance: making sure generative AI is used responsibly, particularly in regulated environments
Building a small generative AI application end to end, or taking ownership of LLM work in your current role, is often the most practical way to build experience. Your existing production and monitoring skills are a real advantage here.
Gravitas were extremely responsive and really took the time to understand our requirements & the context of our organisation
Dynamics Client 1
Head of People, Dynamics Client
Excellent service & support... a very positive experience!
Data Client 1
Co-Founder & Technical Lead, Data Client
Gravitas understands what we need & are very quick to find the candidates!
Public Sector Client 1
Head of Digital, Public Sector Client
Easy to connect! Gravitas understood the brief, good communication & easy process!
Housing Client 1
Group Director, Housing Client
