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Tier-1 brand, metro location, and mid-level role increase competition, while niche Agentic AI skills moderate density.
Requires specialized Agentic AI and LLM expertise, though core ML engineering skills remain broadly transferable.
Explicit 6+ years and multiple mandatory AI, Python, orchestration, and deployment technology requirements increase strictness.
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Design, develop, and deploy intelligent Agentic AI solutions leveraging LangChain, LangGraph, and orchestration frameworks to automate complex enterprise workflows.
Build Retrieval-Augmented Generation (RAG) pipelines with vector databases, embeddings, and enterprise knowledge sources; integrate AI agents using Model Context Protocol (MCP) and Google Agent-to-Agent (A2A) standards.
Develop scalable backend services in Python/FastAPI and deploy AI-powered applications using Docker, Kubernetes/OpenShift, working with cloud-native infrastructure and enterprise data platforms like Snowflake.
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field.
3+ years of experience in Python development and AI/ML application development.
Strong hands-on experience with Python, LangChain, LangGraph, FastAPI, SQL, building RAG pipelines, embeddings, vector databases, LLMs such as GPT or Claude, Docker, Kubernetes/OpenShift, and CI/CD pipelines.
Work Experience Required: 3+ years in Python and AI/ML development.
Experienced in Agentic AI architectures, multi-agent systems, and AI orchestration frameworks with practical knowledge of MCP and Google A2A standards.
Skilled in integrating AI solutions with enterprise data platforms (e.g., Snowflake) and cloud platforms (Azure, AWS, or GCP).
Strong backend and AI service development expertise demonstrated through working with Python, FastAPI, containerization, and large-scale AI deployments involving LLMs and RAG.