Job Description

HFM is an internationally acclaimed multi-asset broker, delivering cutting-edge trading tools, platforms, and conditions to traders worldwide. We are committed to innovation, transparency, and excellence in the financial markets.

As we continue to expand, we are seeking a driven and strategic Mid AI Engineer to join our team.

Responsibilities:

Build and ship internal AI services end to end — from the data pipeline through the model to the API other teams call — working within the team's established architecture.
Develop asynchronous REST APIs and microservices in FastAPI, deployed on Kubernetes, to serve models and orchestrate agent tool calls.
Implement retrieval pipelines over PostgreSQL and pgvector — chunking, embedding, indexing and retrieval tuning — and measure them against a held-out evaluation set.
Build and maintain training pipelines covering data preparation, fine-tuning, evaluation and model registration, promoting a new model only when it measurably beats the one in production.
Integrate agent tools and Model Context Protocol (MCP) endpoints, ensuring each tool enforces its own access controls rather than relying on the model to do so.
Write the evaluation sets, structural output checks and drift monitoring that prove a model works before release and keep proving it afterwards.
Instrument services with structured logging, Prometheus metrics and OpenTelemetry tracing, so that failures can be diagnosed rather than guessed at.
Review the work of junior engineers and trainees, pair with them on delivery, and contribute to the team's engineering and documentation standards.

Requirements

Bachelor's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field.
3+ years of backend software development experience, including hands-on delivery of at least one machine learning or LLM application into production.
Strong proficiency in Python and FastAPI, with solid working knowledge of PostgreSQL and SQL.
Practical experience with LLM application patterns — retrieval-augmented generation, prompt design, structured outputs, and evaluation of model quality.
Experience with Docker, Git and CI/CD pipelines, and comfort working in a containerised environment such as Kubernetes.
Working knowledge of a major cloud platform, ideally AWS.
A methodical approach to correctness: you write tests, you measure before and after, and you can explain how you know a change is an improvement.
Strong communication skills and full professional proficiency in English.
A collaborative approach — comfortable learning from senior colleagues and supporting those with less experience.

Nice to have:

Experience running vector search in production (pgvector, OpenSearch, Qdrant or similar).
Exposure to multi-agent frameworks (e.g., LangGraph, CrewAI, AutoGen, LlamaIndex) or to the Model Context Protocol.
Experience with model registries and deployment workflows (MLflow or equivalent), or with serving open-weight models (e.g., vLLM).
Familiarity with analytical data stores such as ClickHouse, and with observability tooling (OpenTelemetry, Grafana, Langfuse).
Familiarity with FinTech platforms, FX trading, or financial services operations.

Resumes must be submitted in English.
Applicants must be eligible or have legal authorization to work in the country where the position is based.