Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience)
5+ years of Data or ML Engineering experience, with at least 3 years shipping AI or ML systems to production.
Strong Python skills (typed code, async, testing) and solid SQL fluency.
Hands-on experience building agentic applications with frameworks such as LangGraph, LlamaIndex, CrewAI, or the Anthropic/OpenAI Agents SDKs — including tool use, memory, and multi-step reasoning patterns.
Practical experience with MCP or comparable tool/function-calling protocols; comfortable designing tool schemas and sub-agent boundaries.
Experience with RAG architectures, vector stores (e.g. pgvector, Pinecone, Weaviate), and embedding models
Familiarity with at least one major cloud provider (GCP, AWS, Azure) and deploying data solutions in the cloud
Strong DevOps fundamentals: CI/CD (GitHub Actions, Cloud Build, or similar), IaC (Terraform), containerisation (Docker), and orchestration (Kubernetes or serverless equivalents)
Comfortable building and maintaining data pipelines with orchestrators (Airflow/Composer, Dagster) and distributed engines (Spark, BigQuery)
Strong troubleshooting mindset: ability to debug issues across data, infra, pipelines, and deployments
Collaborative mindset and clear communication across engineering, analytics, and business stakeholders