The Role

We’re looking for a Senior AI/ML Engineer to lead the design and deployment of large language model solutions across our client portfolio. This is a high-impact individual contributor role, sitting at the intersection of applied research and production engineering. You’ll work closely with our Head of AI and cross-functional product teams to bring GenAI capabilities from prototype to production.

What You’ll Do

• Design, fine-tune, and deploy LLM-based systems including RAG pipelines, agents, and prompt engineering frameworks at enterprise scale
• Lead technical evaluation of foundation models (GPT-4, Claude, Llama, Mistral) and select the right approach for each use case
• Build robust ML infrastructure for model serving, monitoring, and evaluation, ensuring reliability and cost efficiency in production
• Collaborate with data engineers to integrate AI capabilities into existing data platforms and pipelines
• Define best practices for responsible AI development, including bias mitigation, hallucination detection, and output evaluation
• Mentor mid-level engineers and contribute to the broader ML community through internal knowledge sharing
• Engage directly with clients on technical scoping, solution design, and delivery

What We’re Looking For

• 5+ years of experience in machine learning or AI engineering, with at least 2 years focused on LLMs or generative AI
• Strong Python skills and hands-on experience with frameworks such as LangChain, LlamaIndex, HuggingFace Transformers, or similar
• Proven track record of taking ML models from experimentation to scalable production deployment
• Deep understanding of transformer architectures, prompt engineering, RLHF, and fine-tuning techniques (LoRA, QLoRA, PEFT)
• Experience with cloud platforms (AWS, GCP, or Azure) and containerised ML workflows (Docker, Kubernetes)
• Comfortable working with vector databases (Pinecone, Weaviate, pgvector) and building retrieval-augmented generation systems
• Strong communication skills — able to translate complex AI concepts for non-technical stakeholders

Nice to Have

• Experience in financial services, insurtech, or regulated data environments
• Contributions to open-source ML projects or published research
• Familiarity with MLOps tooling (MLflow, Weights & Biases, Vertex AI, SageMaker)
• Knowledge of data governance, model cards, and EU AI Act compliance considerations