Machine Learning Engineer

Machine Learning Engineer

King's Crosss

£150,000 - £175,000

We are hiring four people at senior and lead levels. Senior engineers own major capabilities, lead engineers also set technical direction and mentor others.

About Descrial

Descrial is a startup inside a twenty-year-old company

Tres Vista has run the analytical work behind private capital since 2006. 2,000 people, 400+ clients including some of the biggest names in the industry, no outside capital and no outside board. That was the first chapter. And we enjoyed getting here!

The next chapter is harder and more interesting

We are moving from services to software, from one firm's workflows to an industry's, and from executing investment decisions to shaping how they get made. Thirty of us in King's Cross, small and senior, with day one founder intensity.

At its fullest expression, Descrial changes how every serious investment firm decides.

The role

You report to the Head of Engineering and work hands-on on the UK core team. Our environment spans Lang Graph and Lang Chain, leading model providers, Python/FastAPI services, and vector and graph retrieval. Token cost, latency and auditability are engineering constraints throughout.

Your remit

The agents and model layer

You build Lang Graph/Lang Chain workflows with state, checkpoints, tool calls and human approval patterns. Pydantic-validated prompts and structured outputs support extraction, summarisation, classification and reasoning over investment documents. You integrate Anthropic, OpenAI and Amazon Bedrock, choose models by task and build fallbacks that absorb outages and provider changes. You manage context windows and compaction across long runs to control cost, and build and consume MCP tool servers for safe access to services, search and documents.

The retrieval

You own parsing, chunking, embeddings, Qdrant vector retrieval, reranking and hybrid search in RAG pipelines. You extend GraphRAG with Neo4j, Cypher and graph data modelling. Relevance evaluations guide improvements while indexed content stays isolated across tenants.

The evaluation and operation

You build golden datasets, LLM-as-judge evaluations and regression gates for prompt and model changes. Lang Smith/Lang Fuse-class instrumentation makes latency, cost and quality visible for every model call. You apply tool-use constraints, output validation and data-privacy guardrails in the UK/GDPR context. You ship Python 3.11+/FastAPI services with PostgreSQL and operate on AWS through CI/CD quality gates.

Your experience

* You have 5+ years in software engineering with strong Python, including 2+ years building LLM or ML systems used in production.

* You have deep hands-on agent/orchestration experience with Lang Graph, Lang Chain or comparable frameworks beyond prototypes.

* You have production RAG experience with embeddings, vector databases, chunking and retrieval evaluation.

* You have graph-backed retrieval experience or graph-modelling foundations that let you learn Neo4j and Cypher quickly.

* You can describe an evaluation harness you built and how it governed changes.

* You use AI coding tools daily, such as Cursor or Claude Code, and have a considered view of where they help.

* You have a degree in engineering, computer science or a related field, or equivalent experience from a strong technical background.

Useful to have, none required

* Qdrant, Weaviate or pgvector in production, and ingestion tools such as Docling or unstructured

* MCP server development and observability tools such as Lang Smith, Lang Fuse or Ragas

* Fine-tuning, embedding-model selection or open-weight deployment

* Financial documents such as filings, fund documents and research

The reality

This will be difficult.

It demands pace and accuracy. We are building…