Role Summary

We are looking for a Python engineer with strong AI and NLP experience to join a data analytics initiative. This is a hands-on engineering role combining software development, applied NLP, and LLM integration.

Key Responsibilities

●      Design and build Python-based data pipelines

●      Integrate with LLM APIs as part of an automated analysis workflow

●      Develop logic to analyze and interpret various forms of input data

●      Design evaluation frameworks that produce consistent, explainable, and repeatable results

●      Build reusable, testable, production-quality code (not just prototypes/notebooks)

●      Collaborate with stakeholders to refine evaluation criteria and benchmarks

●      Optimize pipelines for scale and reliability

Required Skills & Experience

●      Python: Strong professional experience in production-grade code, not just scripting

●      LLM APIs: Hands-on experience integrating and working with LLM APIs (e.g. Google Vertex AI/Gemini) — including prompt design, structured output handling, and response evaluation

●      Document parsing/ingestion: Experience across common formats (e.g., PDF, HTML, DOCX), including messy or inconsistent sources

●      NLP fundamentals: Solid grounding in embeddings, semantic similarity, text classification, quality/readability analysis

●      Structured & semi-structured data: Experience handling this alongside unstructured text

●      Content organization: Ability to reason about structure (e.g., formatting, hierarchy) as an input to automated analysis

●      Data pipelines/ETL: Experience building pipelines for large-scale text processing

●      API integration: Familiarity with working within a cloud ecosystem (Google Cloud experience is a plus)

●      Evaluation/scoring frameworks: Experience designing or implementing frameworks for qualitative-to-quantitative analysis

●      Software engineering fundamentals: Git, testing, code review, CI/CD

Nice to Have

●      Experience with retrieval-based or knowledge-base-driven AI systems

●      Experience with vector databases or embeddings-based search

●      Background in content strategy, technical writing, or information architecture

●      Prior work with Google Cloud AI tooling