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

Apply Now

Machine Learning Engineer - LLMs & Agentic AI (Lead I - Data Science)

UST
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist, Payments - ML in Production

Head of Data Science Technology (Product, Engineering, Design) · London ·

Lead Data Engineer

Data Scientist (Semantic Search / Recommender Systems)

Senior Data Science Consultant, AWS Professional Services

Data Scientist (Based in Dubai)

Overview

Role description


Machine Learning Engineer - LLMs & Agentic AI


London (Hybrid - 3 days onsite). Full-time, Permanent. Join by December 2025.


We are seeking a skilled and forward-thinking Machine Learning Engineer to join our Applied AI and R&D team in London. You'll play a key role in building the next generation of agentic AI systems that enhance Oversight's enterprise spend management and risk control solutions.


This hands-on role combines deep machine learning expertise with cutting-edge work on Large Language Models (LLMs), Small Language Models (SLMs), retrieval-augmented generation (RAG), and multi-agent frameworks. You'll collaborate with data engineers, AI researchers, and product teams to design and deploy scalable, explainable, and autonomous AI solutions.


Responsibilities

  • Design, fine-tune, and deploy ML/LLM models for production.
  • Build and optimize RAG pipelines using vector databases and embeddings.
  • Develop multi-agent workflows leveraging LangChain, LangGraph, or MCP.
  • Implement prompt engineering, safety, and explainability modules in agentic systems.
  • Collaborate with data teams on real-time pipelines, model monitoring, and drift detection.
  • Experiment with AI/GenAI frameworks and translate research prototypes into production-ready solutions.
  • Contribute to best practices in ML engineering, mentoring, and technical documentation.

Qualifications

  • Bachelor's or Master's in Computer Science, Machine Learning, or related field.
  • Minimum of 3+ years experience building and deploying ML systems.
  • Proficiency in Python, PyTorch, TensorFlow, Hugging Face Transformers.
  • Experience with LLMs/SLMs (e.g., GPT-4, Claude, Gemini, LLaMA).
  • Strong skills in RAG, vector databases, and data engineering (SQL, Spark, Ray).
  • Familiarity with cloud ML platforms (AWS, Azure, or Databricks ML).
  • Understanding of MLOps, CI/CD, and infrastructure-as-code.

Nice to Have

  • Experience with agentic frameworks
  • AI safety/guardrails
  • Anomaly detection / open-source AI contributions


#J-18808-Ljbffr

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.

Jobs

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

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Jobs

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Jobs

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.