ML Engineer / Senior ML Engineer – GenAI & LLM
Location: London, UK (Hybrid – 3 Days Onsite)
Contract Duration: 12 Months
We are looking for an experienced ML Engineer / Senior ML Engineer with strong expertise in Azure, Machine Learning Engineering, LLMs, and Generative AI to join a growing AI engineering team. The role involves designing, developing, deploying, and maintaining enterprise-scale AI/ML and GenAI solutions in production environments.
The ideal candidate will have hands-on experience in LLM application development, RAG pipelines, MLOps, model deployment, AI infrastructure, and scalable cloud-based ML systems.
Required Skills
* Strong experience with Azure / Azure ML
* Hands-on experience in Machine Learning Engineering (MLE)
* Expertise in LLMs (Large Language Models)
* Experience in Generative AI
* Strong Python and SQL skills
* Experience with Docker & Kubernetes
* Knowledge of CI/CD pipelines and MLOps
* Experience with RAG architectures, vector databases, and embeddings
* Prompt Engineering experience
* Experience with LLM fine-tuning techniques such as:
* LoRA
* QLoRA
* PEFT
Nice to Have
* Insurance / InsurTech domain experience
Experience Required
* 5–8+ years of relevant experience
Key Responsibilities
AI & ML Solution Development
* Design, build, and deploy scalable AI/ML and Generative AI solutions.
* Collaborate with business stakeholders and data scientists to develop intelligent AI systems and architectures.
LLM & Generative AI Engineering
* Develop enterprise-grade LLM applications and GenAI solutions.
* Build and implement:
* RAG pipelines
* AI Agents / Agentic systems
* Embedding workflows
* Vector search systems
* Fine-tune pretrained LLMs using LoRA, QLoRA, and PEFT techniques.
* Create effective prompts and integrate LLMs with enterprise APIs and platforms.
Data Engineering & Feature Engineering
* Design and maintain robust ETL/ELT pipelines.
* Integrate structured and unstructured data from multiple sources into centralized platforms.
* Perform feature engineering and optimize data workflows.
MLOps & Deployment
* Deploy AI/ML models into production securely and efficiently.
* Build automated CI/CD pipelines for model training, testing, deployment, and monitoring.
* Manage end-to-end AI model lifecycle processes.
Monitoring & Optimization
* Monitor deployed models for:
* Prediction accuracy
* Latency
* Resource utilization
* Reliability
* Troubleshoot and optimize production AI systems.
Infrastructure & Cloud Management
* Manage AI infrastructure using Azure cloud technologies.
* Work with containerization and orchestration tools such as Docker and Kubernetes.
Responsible AI & Governance
* Ensure AI systems are secure, compliant, transparent, explainable, and unbiased.
* Implement governance, versioning, monitoring, and rollback strategies.
Collaboration & Documentation
* Work closely with Data Scientists, DevOps Engineers, Software Engineers, and Business Teams.
* Maintain detailed technical documentation throughout the AI/ML lifecycle.
Preferred Technical Stack
* Azure AI / Azure ML
* Python
* SQL
* Docker
* Kubernetes
* LangChain / LLM orchestration frameworks
* Vector Databases
* CI/CD & MLOps tools
* Prompt Engineering
* RAG Frameworks
* GenAI Platforms