We’re looking for a Senior AI Engineer to help build production-grade AI systems with a strong focus on retrieval quality, agent orchestration, and reliability at scale. You would take ownership of the full RAG pipeline, from document ingestion and chunking strategy through retrieval design, evaluation, model integration, and stateful agent workflows. The work sits at the intersection of applied LLM systems, backend engineering, and production architecture, so it will suit someone who enjoys turning complex ideas into robust, scalable systems that work in the real world. You’ll join a team that values strong ownership, thoughtful system design, practical problem solving, and collaboration. The environment is engineering-led and hands-on, with a focus on building solutions that are not only technically interesting, but also observable, maintainable, and trustworthy in production.
Key Responsibilities:
- Chunking and ingestion architecture — deterministic, validation-gated pipelines handling diverse enterprise document types with indexing and contextual enrichment
- Retrieval pipeline — Vector search, BM25 hybrid retrieval, RRF re-ranking, embedding model selection and optimisation
- Agent orchestration — designing stateful, multi-step agent workflows with LangGraph; building chains and tool-use patterns with LangChain
- Observability — tracing, debugging, and monitoring pipeline performance end-to-end with LangSmith; catching retrieval and reasoning failures before they reach users
- Model evaluation and tuning — RAGAS-based evaluation frameworks, benchmarking retrieval recall and answer faithfulness, iterating toward production quality
- Open-weight LLM deployment — evaluating and shipping models (Llama 4, Qwen3, MiMo-V2) under memory and latency budgets via Amazon Bedrock and self-hosted inference
- Workflow orchestration — async pipeline design with Temporal.io; containerised deployment on Kubernetes via GitLab CI
Required Qualifications:
- Strong software engineering skills in .NET and/or Python, with experience building production-grade APIs and services
- Applied algorithmic problem solving, including search, ranking, clustering, and data extraction/transformation pipelines
- Solid understanding of LLM architectures and trade-offs (e.g. GPT-family, Claude, Llama, and similar models)
- Experience integrating LLM and agent APIs (e.g. Azure OpenAI, OpenAI API, Claude API, Amazon Bedrock)
- Design and implementation of agent-based systems for automation, orchestration, and decision support
- Experience working with relational, document, and graph databases, including data modeling and querying
- Web data ingestion experience, including web scraping, schema extraction, and handling changing source structures
- Experience building reliable, scalable, maintainable AI systems, including logging, error handling, and performance considerations
Nice to have:
- Fine-tuning and preference optimization techniques (e.g. supervised fine-tuning, RL-style approaches via APIs)
- Designing pipelines that convert unstructured data into reliable structured representations
- Exposure to event-driven or background processing architectures for long-running AI workflows
- Experience contributing to or operating AI-powered systems in production