AI / ML Engineer

AI/ML Engineer (SDE2) who can design, build, and operate production-grade AI systems, with a strong focus on LLM applications, retrieval, memory, and personalisation. This role is suited to an engineer who combines strong software engineering fundamentals with hands-on experience building real-world AI applications—not just notebooks, model experiments, or basic LLM API integrations.

The candidate will have responsibilities across the following functions: Build Conversational AI Systems:

• Develop production-ready conversational AI experiences using LLMs, structured context, and user-specific information.
• Build systems that maintain conversational context, retrieve relevant past information, and generate personalised responses.
• Improve context selection, follow-up handling, and response consistency across conversations.

Build RAG, Retrieval and Memory Systems:

• Design and improve retrieval-augmented generation (RAG) pipelines using structured data, embeddings, vector search, and graph-based retrieval where appropriate.
• Build long-term memory and user-profile systems that extract, store, update, rank, and retrieve relevant information.
• Handle conflicting information, memory freshness, relevance scoring, deduplication, and context selection.
• Improve retrieval quality, relevance, and personalisation through measurable evaluation.

Develop LLM Orchestration and Optimisation:

• Design multi-step AI pipelines involving query understanding, retrieval, reasoning, and response generation.
• Optimise model selection, prompt design, tool calls, and pipeline execution.
• Reduce unnecessary LLM calls while balancing quality, latency, and cost.
• Implement fallbacks, retries, timeouts, and graceful degradation for production reliability.

Build Personalisation and User Intelligence:

• Develop systems that adapt to user preferences, goals, interests, historical interactions, and evolving context.
• Combine structured user profiles, conversational memory, and retrieved knowledge to improve relevance.
• Explore lightweight ML approaches for classification, ranking, recommendation, and context selection where they outperform additional LLM calls.

Evaluate and Improve AI Quality:

• Design evaluation datasets and test cases for retrieval quality, memory accuracy, personalisation, and response consistency.
• Measure quality using appropriate offline evaluations and production metrics.
• Build regression tests to catch failures in prompts, retrieval, memory updates, and model behaviour.
• Use evaluation results and production feedback to drive improvements.

Own Production AI Engineering: