Data Engineer
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Summary
Senior data engineer (5+ years) delivering Agentic AI, GenAI, ML, and BI solutions within client enterprise systems: expert Python/SQL with Spark, LangChain, Databricks, Airflow/Dagster, plus MLOps/LLMOps (LangSmith, Opik, Langfuse), Terraform, and work across AWS, Azure, and GCP.
- Degree in Computer Science, Engineering, or equivalent professional experience.
- 5+ years of relevant professional experience in data engineering, including leading technical workstreams, mentoring junior engineers, and driving software engineering best practices across teams.
- Expert-level proficiency in Python and SQL, with the ability to work effectively in polyglot environments, including Scala and Java, within client enterprise systems.
- Strong experience building Agentic AI, Generative AI, Machine Learning, and Business Intelligence solutions, including:
- RAG and embeddings
- Context construction
- Production output handling
- Modern frameworks such as Spark, LangChain, Databricks, Dask, Airflow, Dagster, and Kedro
- Proven ability to lead AI feature delivery end to end, with strong judgment around model behaviour, evaluation, reliability, guardrails, and the trade-offs between quality, latency, and cost.
- Experience implementing data security and governance controls, including PII/PHI management, authentication, and Role-Based Access Control (RBAC).
- Deep knowledge of MLOps and LLMOps, including:
- CI/CD for data workflows
- Automated agent evaluation using LangSmith, Opik, and Langfuse
- Infrastructure as Code using Terraform
- Experience across AWS, Azure, and GCP
- Excellent time management and autonomy, with the ability to independently own and deliver technical workstreams.
- Experience using AI coding agents such as Cursor, Claude Code, and Codex is an advantage.
- Strong written and verbal communication skills in English and the relevant local office language(s).