The Role
Build and deploy production AI services, including data pipelines, LLM applications, retrieval systems, asynchronous APIs, microservices, model training workflows, agent tools, evaluation frameworks, and observability. The role uses FastAPI, Kubernetes, PostgreSQL, and vector search, while supporting model quality, access controls, testing, documentation, and mentoring junior engineers.
Summary Generated by Built In
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:
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.
Applicants must be eligible or have legal authorization to work in the country where the position is based.
Benefits
- Hybrid
Work Model (2 days working from home)
- Monthly
Food Vouchers
- Comprehensive
Health & Life Insurance
- Summer
Short Fridays (August)
- Additional
Paid Annual Leave
- Birthday
Leave
- Udemy
Business access
- Referral
Bonus Program
Additional Support
- Visa
Sponsorship and Relocation Assistance
Sounds like you?
Let’s write the next chapter together!
All applications will be handled with the strictest
confidentiality.
Skills Required
- Bachelor's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field
- 3+ years of backend software development experience
- Hands-on delivery of at least one machine learning or LLM application into production
- Strong proficiency in Python and FastAPI
- Solid working knowledge of PostgreSQL and SQL
- Practical experience with retrieval-augmented generation, prompt design, structured outputs, and model-quality evaluation
- Experience with Docker, Git, and CI/CD pipelines
- Comfort working in a containerized environment such as Kubernetes
- Working knowledge of a major cloud platform, ideally AWS
- Experience writing tests and measuring changes for correctness and improvement
- Strong communication skills and full professional proficiency in English
- Collaborative approach, including learning from senior colleagues and supporting less experienced colleagues
- Experience running vector search in production using pgvector, OpenSearch, Qdrant, or similar
- Exposure to multi-agent frameworks or the Model Context Protocol
- Experience with model registries and deployment workflows such as MLflow, or serving open-weight models such as vLLM
- Familiarity with ClickHouse and observability tools such as OpenTelemetry, Grafana, or Langfuse
- Familiarity with FinTech platforms, FX trading, or financial services operations
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The Company
What We Do
HFM is an internationally acclaimed, regulated multi-asset broker serving retail and institutional clients worldwide. The company provides online trading services and platforms across forex, contracts for difference (CFDs), futures, commodities, stocks, and indices. Its offering emphasizes competitive trading conditions, innovative tools, partner support, and access to multiple global markets through its financial technology infrastructure, helping traders participate in global markets.
