Machine Learning 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).