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Metro location and hot GenAI demand increase applicants, but seniority and niche LLM skills reduce density.
Low - GenAI, LLM, and Python skills are highly transferable across industries.
High - strict mandatory GenAI, Python, vector DB and AWS skill requirements filter candidates strongly.
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Design and develop Generative AI-powered applications using Python and LLM orchestration frameworks like LangChain, LangGraph, and OpenAI tools.
Build and enhance retrieval-augmented generation (RAG) pipelines involving embeddings, vector databases (e.g., PGVector, FAISS), and indexing optimizations.
Contribute to code reviews, design discussions, documentation, and follow best coding, testing, and DevOps practices within the team.
Strong hands-on experience with Python (including Flask, REST APIs, async programming).
Practical experience working with Generative AI, LLM-based applications, and RAG systems using embeddings and vector databases.
Familiarity with Agentic frameworks such as LangChain 1.0, LangGraph, ReAct, OpenAI Assistants, or custom agents.
Experience with AWS Cloud services (ECS, S3, API Gateway, IAM, Bedrock), microservices, containerization (Docker), and CI/CD pipelines (GitLab/GitHub Actions). Work Experience Required: Not explicitly mentioned in the JD.
Experienced in implementing GenAI applications with practical knowledge of LLM orchestration and RAG pipeline components showing ability to translate functional requirements into scalable Python services.
Comfortable working within cloud environments, especially AWS, and using containerization and DevOps tools to support application deployment and lifecycle.
Collaborative mindset evidenced by participation in design reviews, documentation, and adherence to coding standards in team settings.