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

Apply Now

Machine Learning Engineer (London Area)

Mastek
London
6 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer - London

Machine Learning Engineer

Machine Learning Engineer (AI infra)

Machine Learning Engineer

Machine Learning Engineer (Databricks)

Machine Learning Engineer (Reinforcement Learning)

Location: London, UK (3 days in office)

SC Cleared: Required

Job Type: Full-Time

Experience: 10 –15 years (Relevant exp 5+ years)


Job Summary:We are seeking an experienced Machine Learning Engineer with expertise in big programmes and has contributed to the delivery of complex business cloud solutions. The ideal candidate will have a strong background in Machine Learning engineering and an expert in operationalising models in the Databricks MLFlow environment (chosen MLOps Platform).


Responsibilities:

  • Collaborate with Data Scientists and operationalise the model with auditing enabled, ensure the run can be reproduced if needed.
  • Implement Databricks best practices in building and maintaining economic modelling (Machine Learning) pipelines.
  • Ensure the models are modular.
  • Ensure the model is source controlled with agreed release numbering.
  • Extract any hard-coded elements and parameterise them so that the model execution can be controlled through input parameters.
  • Ensure the model input parameters are version controlled and logged to the model execution runs for auditability.
  • Ensure model metrics are logged to the model runs.
  • Ensure model logging, monitoring, alerting to make sure any failure points are captured, monitored and alerted for support team to investigate or re-run
  • If the model involves running of multiple experiments and chooses the best model (champion challenger) based on the accuracy/error rate of each experiment, ensure this is done in an automated manner.
  • Ensure the model is triggered to run as per the defined schedule.
  • If the process involves executing multiple models feeding each other to produce the final business outcome, orchestrate them to run based on the defined dependencies.
  • Define and Maintain the ML Frameworks (Python, R & MATLAB templates) with any common reusable code that might emerge as part of model developments/operationalisation for future models to benefit.
  • Where applicable, capture data drift, concept drift, model performance degradation signals and ensure model retrain.
  • Implement CI/CD pipelines for ML models and automate the deployment.
  • Maintain relevant documentation.

Requirements:

  • Bachelor's degree in a relevant field.
  • Minimum of 5 years of experience as a business analyst, with a focus on capturing and documenting business requirements and business processes.
  • Strong understanding of banking and financial industry practices and regulations.
  • Solid knowledge of Data Management process, data analysis and modeling techniques.
  • Experience in monetary policy analysis (nice to have)
  • Experience in time series database analysis
  • Familiarity with business intelligence tools and concepts.
  • Strong analytical and problem-solving skills.
  • Experience in managing software development lifecycles within Agile frameworks to ensure timely and high-quality delivery.
  • Excellent communication and collaboration skills.
  • Ability to adapt to changing requirements and priorities in a fast-paced environment.

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.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.