Senior Ai Engineer

## About the Role We are seeking a technically exceptional Senior AI Engineer to lead the Generative AI and ML Engineering function within our Operational Intelligence Program. This is a hands-on engineering leadership role responsible for architecting and delivering next-generation LLM, agentic, and multimodal AI systems that drive business growth, elevate customer experience, and ensure secure, scalable, production-grade AI at enterprise scale. As the technical authority for GenAI engineering, you will set the engineering standard across agentic orchestration, retrieval-augmented generation, LLMOps, and responsible AI — while building and mentoring a team of GenAI engineers committed to continuous learning and engineering excellence. ## Key Responsibilities - You will lead the end-to-end design and development of multi-agent AI systems using frameworks such as LangGraph, CrewAI, and AutoGen, enabling autonomous reasoning, tool use, inter-agent coordination, and adaptive decision-making at enterprise scale. - Alongside this, you will design and operationalise multimodal generative AI pipelines that unify text, image, tabular, and graph data using transformer-based architectures — including BERT, CLIP, LLaVA, T5, Whisper, GPT-4o, and Gemini — to deliver rich, cross-modal intelligence across the business. - A core part of the role involves building production-grade RAG and Graph-RAG systems integrating vector databases such as Pinecone, pgvector, and OpenSearch, alongside knowledge graphs including Neo4j and AWS Neptune, for semantic retrieval, entity-aware reasoning, and grounded generation. - You will also lead LLM fine-tuning, prompt engineering, and model alignment strategies — including RLHF, PEFT, LoRA, and instruction tuning — to adapt foundation models for specialised enterprise use cases. - You will be responsible for establishing robust LLMOps and MLOps pipelines on Databricks running on AWS, incorporating MLflow, feature stores, prompt evaluation frameworks, model lineage tracking, and continuous retraining workflows to ensure reliable and auditable AI delivery. - In parallel, you will develop high-performance Python backend services for LLM inference orchestration, async job handling, streaming responses, and distributed data workflows capable of supporting high-throughput GenAI operations. - The role requires engineering state, memory, and context management subsystems that enable agents to reason temporally, maintain session continuity, manage long-context windows, and coordinate seamlessly across tools and modalities. - You will implement Responsible AI and AI governance practices across all systems — including bias detection, hallucination mitigation, explainability dashboards, output safety guardrails, and compliance with data ethics standards — ensuring the transparency and fairness of every deployed model. - Beyond the GenAI stack, you will apply traditional ML and statistical modelling techniques — including regression, clustering, forecasting, and ensemble methods — within hybrid architectures alongside LLMs to deliver interpretable, explainability-first decision systems. - You will continuously research, evaluate, and productionise advancements in generative modelling, agentic AI, multimodal transformers, and frontier foundation models, benchmarking all systems against enterprise-scale performance and safety requirements. ## What We Are Looking For - The ideal candidate holds a Master's or Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related engineering discipline, and brings significant hands-on experience leading and delivering complex GenAI or ML engineering programmes in production environments. - You will have expert-level, demonstrable experience designing, building, and deploying large language model applications, agentic systems, and RAG pipelines from prototype through to production. Deep proficiency across the LLM ecosystem is essential — including OpenAI, Anthropic, Gemini, Hugging Face, LangChain and LangGraph, and open-source foundation models such as LLaMA, Mistral, and Falcon. - You will bring a strong command of GenAI engineering patterns: prompt engineering, chain-of-thought reasoning, tool and function calling, vector embeddings, semantic search, and agent memory architectures. - Solid applied knowledge of ML fundamentals — including predictive modelling, deep learning using PyTorch and TensorFlow, and statistical techniques — is expected, used in tandem with GenAI for hybrid, interpretable systems. - Excellent Python engineering skills are required, including async programming, API development with FastAPI, and building inference-ready microservices, alongside SQL proficiency. Hands-on experience with cloud AI infrastructure — specifically AWS SageMaker, Amazon Bedrock, Azure OpenAI, or GCP Vertex AI — is essential, as is familiarity with LLMOps and MLOps tooling including MLflow and Weights & Biases. - Finally, you will bring strong analytical, communication, and stakeholder management skills — with the ability to translate complex GenAI concepts into clear business value and lead cross-functional teams confidently toward delivery outcomes.