Machine Learning Engineer

About the role

We are building an internal AI platform that powers agents, accelerates information retrieval and automates workflows, as well as an ML platform for training, versioning and scheduling large‑scale models. You will help shape these platforms and enable enterprise‑wide AI adoption.

Key responsibilities

• Develop, maintain and debug platform services, integrating third‑party APIs, internal systems, databases, object storage and model providers with robust error handling.
• Connect AI solutions to internal applications, databases, APIs and enterprise knowledge sources.
• Write unit and integration tests, review code, keep dependencies up‑to‑date and ensure services are observable.
• Collaborate with data scientists to translate their needs into reusable pipelines and tooling.
• Contribute reusable components, agentic capabilities and integration patterns that speed AI rollout across the company.

Required profile

• 2+ years of production Python experience, having owned services or libraries used by others.
• Hands‑on experience building LLM applications, managing context windows, token costs, embeddings, retrieval, and tool/function calling.
• Practical data‑science foundation with personal experience training models.
• Strong SQL skills and experience with relational databases, schema migrations, indexing and connection pooling.
• Familiarity with observability stacks (Prometheus, Grafana, ELK) and distributed tracing (OpenTelemetry, Jaeger/Tempo).
• Experience deploying, inspecting and debugging workloads on Kubernetes with Helm.

Required skills

• Python, FastAPI, Flask, async programming, structured logging.
• LLM development, embeddings, knowledge bases, tool/function calling.
• SQL, relational databases, schema migrations.
• Prometheus, Grafana, ELK stack, OpenTelemetry, Jaeger, Tempo.
• Kubernetes, Helm, Docker, CI/CD (Azure DevOps, GitLab, GitHub Actions), Linux, Git.
• GitOps tools such as Argo CD or Flux.