Job Description
Job Responsibilities:
\n
● Collaborate with the team to design and develop high quality Web applications using Python, Flask, Django, and related technologies.
\n
● Write clean and efficient code, and ensure code maintainability and reusability.
\n
● Design and implement RAG pipelines on Google Cloud / Vertex AI (chunking, embeddings, indexing, retrieval, reranking, grounding).
\n
● Build agentic workflows (tool use, planning, reflection/guardrails, structured outputs) using Python-first frameworks.
\n
● Perform code reviews to ensure code quality and consistency.
\n
● Conduct testing to ensure application quality and reliability.
\n
● Create and maintain technical documentation for web applications.
\n
● Participate in project planning, estimation, and prioritization.
\n
● Stay up to date with the latest technologies for Python development.
\n
● Define and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iteratively.
\n
● Ship to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practices.
\n
\n
Requirement:
\n
● Experience in software development in Python3.
\n
● Decent understanding of the software development/testing life cycle.
\n
● Knowledge of relational databases (e.g. MySQL, PostgreSQL, etc)slanguage skills
\n
● Experience with version control tools, such as Git.
\n
● Experience building RAG solutions (hybrid search, reranking, chunking strategies, embeddings, prompt + schema design).
\n
● Familiar with at least one agentic framework (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel, AutoGen) and tool/function calling patterns.
\n
\n
Preferred Qualifications:
\n
● 1+ year professional experience in Python web application development with either Flask or Django.
\n
● Experience in RESTful API development in Python.
\n
● Understanding of Python web application frameworks such as Flask or Django.
\n
● Experience with Cloud services, such as AWS.
\n
● Experience with Vertex AI and GCP fundamentals (IAM, logging/monitoring, Cloud Run/GKE, storage).
\n
● Knowledge graphs for RAG (entity linking, graph traversal + retrieval fusion).
\n
● Streaming/messaging (Pub/Sub, Kafka), document pipelines (Document AI), and multilingual retrieval.
\n
● Experience with evaluation tooling (RAGAS, TruLens, custom eval harnesses), prompt/version management.
\n
● Frontend integration (basic React/Next.js) or platform enablement (internal developer tooling).