Senior AI/ML Engineer

About the Role

You will join a team delivering AI solutions across a range of enterprise environments and industry verticals. Your work will span RAG pipeline design, LLM fine-tuning, multi-agent orchestration, and AI observability. You will take ownership of technical decisions and work closely with engineering and product stakeholders to ship reliable, well-evaluated AI systems.

You should be comfortable operating independently, driving experimentation, and translating research findings into production-ready solutions. Experience with responsible AI practices and governance is expected.

Key Responsibilities

• Design and deploy end-to-end AI/ML pipelines for enterprise use cases across industries.
• Build and maintain RAG systems using vector databases, semantic search, and knowledge graph techniques.
• rchitect and implement multi-agent AI systems with specialised agents for distinct reasoning and execution tasks.
• Fine-tune large language models (LLaMA, Mixtral, or equivalent) using SFT, LoRA, and PEFT approaches for domain-specific applications.
• Implement LLMOps pipelines covering model lifecycle management, drift detection, and evaluation using tools such as MLflow, DeepEval, and Weights & Biases.
• Design evaluation frameworks to measure hallucination rates, retrieval accuracy, reasoning consistency, and task-specific performance.
• ntegrate AI solutions with enterprise systems via APIs, MCP and microservices.
• Optimise AI workloads for cost, latency, and throughput across cloud platforms.
• Apply responsible AI and governance standards across all delivered systems.
• Contribute to prompt engineering frameworks and reusable AI workflow components.

Requirements

Experience and Background

• 7 or more years of experience in AI/ML engineering, with at least 3 years focused on LLMs and generative AI systems.
• Demonstrated experience delivering production AI systems at enterprise scale.
• Prior experience in a staff or senior individual contributor role.

Technical Skills

• Strong proficiency in Python; experience with PyTorch and/or TensorFlow.
• Hands-on experience with LLM frameworks including LangChain, LlamaIndex, and LangGraph.
• Experience with vector databases such as Pinecone or Chroma.
• Working knowledge of cloud AI platforms: AWS Bedrock, Azure AI, Google Vertex AI, or Databricks.
• Familiarity with containerisation and CI/CD tooling: Docker, Kubernetes, GitHub Actions, or Jenkins.
• Experience with Transformer architectures and fine-tuning techniques (SFT, LoRA, PEFT, DPO).
• Understanding of MLOps and LLMOps practices including model monitoring and observability.

Nice to Have

• Published research in NLP, ML, or AI-adjacent fields.
• Experience with multimodal AI systems.
• Strong familiarity with Model Context Protocol (MCP).
• Background in multilingual modelling or multi-domain AI applications.