Intellias
Senior Python Engineer (Data Engineering & AI Agents)
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Senior Python Engineer (Data Engineering & AI Agents)
We are looking for a Senior Python Engineer (Data Engineering & AI Agents) to build production-grade Python services and data pipelines over large-scale data stores. The role focuses on data engineering, analytical and query engines, RAG, vector search, AI agents, and secure data access. The ideal candidate combines strong Python engineering skills with hands-on experience in large-scale data processing, AI agent applications, data governance, access control, and data quality, with financial services or capital markets experience being a strong plus.
Requirements:
- 6+ years building production software in Python, with strong engineering fundamentals (testing, performance, clean design).
- Solid data engineering: SQL, columnar formats (e.g. Parquet), pipeline design, and handling datasets large enough that naive approaches don’t scale.
- Hands-on experience with at least one analytical or query engine (e.g. DuckDB, Trino, Spark, ClickHouse).
- Real experience building LLM / agent applications: retrieval (RAG), vector databases, and tool / function calling.
- A working understanding of data governance: cataloguing, metadata, lineage, and access control (RBAC / ABAC).
- An instinct for data quality and trustworthy “golden” sources.
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Will be a plus:
- Financial services / capital markets experience (market data, positions, reference data, time-series stores).
- Experience in on-premise / regulated environments and their constraints (data residency, auditability, “golden copy never moves”).
- Familiarity with semantic layers / knowledge graphs and entity resolution.
- Exposure to policy-as-code (e.g. OPA) or data-access platforms.
- Awareness of how AI agents are secured: identity, scoped access, evaluation and monitoring.
- Consulting or client-facing / pre-sales experience.
Responsibilities:
- Build production-grade Python services and data pipelines over large data stores (columnar / time-series and relational), and the queries that join across them.
- Select and implement the right query or analytical engine for each workload, rather than defaulting to one.
- Build catalogue, metadata, lineage and semantic layers that make data discoverable and consistently understood across teams.
- Implement access control that travels with the data: fusing sensitivity and licensing scope, enforced at the point of use, including for AI agents.
- Build agent-facing data access: retrieval (RAG), vector search, and APIs / MCP servers, with permissions applied before context reaches the model.
- Apply LLMs pragmatically to data work (metadata generation, classification, entity resolution) with humans in the loop and evaluate the quality of what the agents produce.
- Help keep data trustworthy: establish golden sources, deduplication and data-quality checks at the source.
- Contribute to discovery and solutioning: assessing current state, weighing build-vs-adopt, and shaping pragmatic, costed plans.
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At Intellias, where technology takes center stage, people always come before processes. We're dedicated to cultivating a tech-savvy environment that empowers individuals to unlock their true potential and achieve extraordinary results. Our customized benefits not only prioritize your well-being but also charge your professional growth, making this opportunity an ideal match for tech enthusiasts like you.
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