Generative AI Engineer

Job Responsibilities:

● Collaborate with the team to design and develop high quality Web applications using Python, Flask, Django, and related technologies.

● Write clean and efficient code, and ensure code maintainability and reusability.

● Design and implement RAG pipelines on Google Cloud / Vertex AI (chunking, embeddings, indexing, retrieval, reranking, grounding).

● Build agentic workflows (tool use, planning, reflection/guardrails, structured outputs) using Python-first frameworks.

● Perform code reviews to ensure code quality and consistency.

● Conduct testing to ensure application quality and reliability.

● Create and maintain technical documentation for web applications.

● Participate in project planning, estimation, and prioritization.

● Stay up to date with the latest technologies for Python development.

● Define and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iteratively.

● Ship to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practices.

Requirement:

● Experience in software development in Python3.

● Decent understanding of the software development/testing life cycle.

● Knowledge of relational databases (e.g. MySQL, PostgreSQL, etc)slanguage skills

● Experience with version control tools, such as Git.

● Experience building RAG solutions (hybrid search, reranking, chunking strategies, embeddings, prompt + schema design).

● Familiar with at least one agentic framework (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel, AutoGen) and tool/function calling patterns.

Preferred Qualifications:

● 1+ year professional experience in Python web application development with either Flask or Django.

● Experience in RESTful API development in Python.

● Understanding of Python web application frameworks such as Flask or Django.

● Experience with Cloud services, such as AWS.

● Experience with Vertex AI and GCP fundamentals (IAM, logging/monitoring, Cloud Run/GKE, storage).

● Knowledge graphs for RAG (entity linking, graph traversal + retrieval fusion).

● Streaming/messaging (Pub/Sub, Kafka), document pipelines (Document AI), and multilingual retrieval.

● Experience with evaluation tooling (RAGAS, TruLens, custom eval harnesses), prompt/version management.

● Frontend integration (basic React/Next.js) or platform enablement (internal developer tooling).