At a Glance
- Tasks: Lead the design and deployment of cutting-edge ML and GenAI systems in finance.
- Company: Global financial services firm with a focus on innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Join a dynamic team with mentorship opportunities and career advancement.
- Why this job: Make an impact by building scalable AI solutions that transform banking operations.
- Qualifications: Master's in a quantitative field or equivalent experience in ML and data analysis.
The predicted salary is between 66150 - 80850 £ per year.
Global financial services firm | Full time
Build and run production AI at scale. Lead the design, deployment and operation of ML, GenAI and agentic systems for complex payments and banking operations. You will work with product, risk, operations and technology.
The role:
- Own delivery end to end, from problem framing to production rollout
- Build ML, GenAI and multi-agent solutions with clear evaluation, safety and monitoring
- Set production patterns: containerisation, CI/CD, automated testing, model and prompt registries, rollback
- Deploy secure, scalable ML and LLM services via APIs, batch, streaming and event-driven patterns
- Create reusable components: feature engineering, evaluation harnesses, agent frameworks, orchestration, memory management
- Mentor engineers and data scientists through code and design reviews
- Turn model outputs into decisions and trade-offs for non-technical stakeholders
- Maintain model cards, runbooks and audit-ready documentation
Essential:
- Master's in a quantitative field, or equivalent experience
- Strong ML fundamentals and applied data analysis
- Production ML at scale: monitoring, drift, reliability, incident management
- Strong Python engineering: modular OO design, testing, performance tuning
- MLOps and LLMOps: training and serving patterns, feature stores, orchestration
- Offline and online evaluation, with guardrails
- Regulated environment experience: model risk, controls, privacy, security
- Strong stakeholder management
Desirable:
- LLMs, RAG, tool calling, agentic workflows, multi-agent orchestration
- MCP and agent interoperability standards
- PyTorch, TensorFlow, scikit-learn, pandas, SciPy, statsmodels
- AWS (SageMaker, Bedrock), with focus on cost, latency and scaling
- Human-in-the-loop, active learning and labelling strategies
Senior Machine Learning Engineer in London employer: Datatech Analytics
Datatech Analytics is an exceptional employer located in the vibrant City of London, offering a dynamic work culture that fosters collaboration and innovation. As a Trading Operations Graduate Analyst, you will benefit from comprehensive training and mentorship, ensuring your professional growth while working alongside industry experts in a fast-paced environment. With a commitment to employee well-being and a focus on meaningful contributions to global trading operations, Datatech Analytics provides a rewarding career path for ambitious graduates.
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We think you need these skills to ace Senior Machine Learning Engineer in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
