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# Senior Machine Learning Engineer

- **Company:** International SOS
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Continuous Integration, Data Cleansing, Data Warehousing, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, SQL Databases, Feature Engineering, Pytorch, Prompt Engineering, Git, AI Platforms, Scikit Learn, Kubernetes, Xgboost, Machine Learning Operations, GPT, Software Version Control
- **Published:** August 4, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=e6a9732f9237c32e

## About the Role

6+ years in AI/ML engineering or applied machine learning

Strong Python skills with scikit-learn, TensorFlow, PyTorch, or XGBoost, plus experience with LLM frameworks (LangChain/LangGraph) and RAG

Experience with cloud AI services (AWS Bedrock/SageMaker, Azure, or GCP) and vector stores

Proficiency in SQL and working with data warehouses/lakes and embeddings

Familiarity with MLOps/LLMOps, containerisation (Kubernetes), and CI/CD

Understanding of prompt engineering, evaluation harnesses, and guardrails

Strong grasp of ML theory, software engineering practices, and version control (Git)

## Description

We are looking for an experienced Senior Machine Learning Engineer to join our Product team, in Chiswick, West London,

You will be responsible for building production-grade AI capabilities across the platform's generative and agentic paradigms - from retrieval-augmented knowledge assistants and context-aware responses to multi-step agent workflows. The role combines strong machine learning and software engineering skills to deliver grounded, governed, and scalable solutions that move from Lab prototype to Factory production., * Design and implement ML, generative, and agentic AI solutions - RAG pipelines, prompt workflows, tool-calling agents, and predictive models

 * Build grounded retrieval over enterprise knowledge with source citation and tenant isolation
 * Integrate models via the model gateway, applying guardrails, PII redaction, and content safety on every request
 * Develop and maintain agent orchestration, memory, and human-in-the-loop escalation paths
 * Perform data preprocessing, feature engineering, prompt design, and evaluation using enterprise datasets
 * Deploy solutions through MLOps/LLMOps pipelines with monitoring, evaluations, and SLAs
 * Optimise models and prompts for accuracy, latency, cost, and groundedness
 * Run experiments, track metrics against golden sets, and iterate to improve quality
 * Collaborate with AIOps and Security to integrate solutions into CI/CD and production monitoring
 * Support responsible-AI practices, model cards, and version control for every release


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