Senior Machine Learning Engineer

Anson McCade; Greater London; Hybrid; GBP 65,000 - 85,000; Full time

Benefits offered by this job

Flexible working

25 days holiday

Career development support

Bonus scheme

Job summary

A leading technology and digital intelligence organization is seeking a Senior Machine Learning Engineer. This hybrid role focuses on designing and deploying impactful AI solutions. With 4-5 years of experience, candidates will lead experimentation, transition machine learning models to production, and apply responsible AI practices. The position also offers a competitive benefits package including a flexible work environment, career development support, and a £7,000 tax-free DV bonus on completion.

Qualifications

• 4-5 years of hands-on machine learning experience.
• Experience deploying models using Python libraries.
• Familiarity with MLOps tooling for model tracking.

Responsibilities

• Design and develop ML models for various applications.
• Lead experimentation cycles to evaluate solutions.
• Transition validated models into production successfully.

Skills

Hands-on ML experience

Deploying ML models in Python

AWS ML services

Experiment design and evaluation

Communication skills

Tools

scikit-learn

XGBoost

PyTorch

TensorFlow

MLflow

Docker

Terraform

Job description

Are you passionate about building impactful AI solutions and pushing the boundaries of machine learning? Do you want your work to deliver real-world value across critical national infrastructure?

We are seeking a Senior Machine Learning Engineer to join a leading technology and digital intelligence organisation in the UK. In this role, you will design, develop, and deploy machine learning and generative AI solutions that address complex challenges in the national security space.

What You’ll Do

• Design, develop, and iterate ML models for traditional tasks (forecasting, classification, anomaly detection) and GenAI/LLM applications.
• Lead experimentation cycles: define hypotheses, design experiments, evaluate results, and iterate rapidly.
• Transition validated experiments into production‑ready solutions, collaborating with engineers and stakeholders.
• Build and optimise ML pipelines using AWS services and experiment tracking tools.
• Implement robust experiment tracking, model versioning, and reproducibility practices.
• Support production models through monitoring, performance analysis, and continuous improvement.
• Apply responsible AI practices, including model explainability and fairness assessment.
• Mentor junior colleagues and share learnings across the team.

About You

• 4‑5 years of hands‑on ML experience.
• Experience deploying ML models in Python using scikit‑learn, XGBoost, PyTorch, or TensorFlow.
• Experience with AWS ML services (SageMaker, Lambda, S3) in production environments.
• Proof of experiment design, hypothesis testing, and statistical evaluation.
• Proven ability to transition models from experimentation to production with governance and quality controls.
• Familiarity with MLOps tooling such as MLflow, Weights & Biases, or DVC.
• Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures.
• Excellent communication skills, able to convey complex findings to technical and non‑technical audiences.

Nice‑to‑Haves

• Advanced LLM techniques: agents, tool use, and agentic workflows.
• Knowledge of vector databases (Pinecone, Weaviate, pgvector).
• Experience with feature stores (Feast, AWS Feature Store).
• Containerisation and orchestration (Docker, Kubernetes, ECS).
• Infrastructure as Code (Terraform, CloudFormation).
• Large‑scale data processing frameworks (Spark, Dask).
• Experience in regulated industries or handling sensitive data.

Security Clearance

• UKIC DV eligible required.
• £7,000 tax‑free DV bonus on completion (TBC, paid quarterly).

Location

• London‑based hybrid role.
• Up to 3 days per week onsite at customer location once cleared.
• 1 day per week team day in the London office (counts as onsite).

What We Offer

• Flexible, hybrid working with support for work‑life balance.
• 25 days holiday, with options to buy/sell and carry over.
• Competitive benefits including pension, cycle‑to‑work, and lifestyle perks.
• Career development support with dedicated managers and mentoring opportunities.
• Bonus scheme and participation in diversity and support groups.

Seniority level

• Mid‑Senior level

Employment type

• Full‑time

Job function

• Information Technology

Industries

• Technology, Information and Media