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Tier-1 brand plus mid-level AI role increases competition, niche agentic specialization reduces it.
Agentic LLM, Python, and MLOps skills are broadly transferable across industries, so background sensitivity is low.
Explicit 5+ years, 2+ years AI, and mandatory LLM/DevOps/Google ADK stack enforce high shortlisting strictness.
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Lead design, development, and deployment of large-scale agentic AI solutions using frameworks like Google ADK, LangChain, and LangGraph.
Architect and implement multi-agent AI systems integrating LLMs (OpenAI, Anthropic, Google Gemini) with AI capabilities such as RAG pipelines and vector databases.
Develop scalable Python backend services with resilient APIs, secure REST endpoints, and deploy on cloud platforms using CI/CD, containerization, and MLOps practices.
5+ years experience in AI/ML or applications development, with at least 2 years focused on AI, prompt engineering, or agentic AI systems.
Advanced Python expertise including FastAPI, Django, Flask, asyncio, and PySpark for scalable backend development.
Experience with LLMs (OpenAI GPT, Gemini, Claude, Llama), LangChain, LangGraph, vector databases, RAG systems, and AI tools like TensorFlow or PyTorch.
Proficiency with cloud platforms AWS, Azure, or GCP; DevOps tools including Docker, Kubernetes, CI/CD pipelines; and secure REST API design.
Experienced lead developer in agentic AI flow design, capable of architecting complex multi-agent systems with integrated LLMs and AI tooling.
Strong backend software engineer skilled in scalable Python services, microservices architecture, and resilient API development for production environments.
Practitioner familiar with cloud deployment, MLOps, containerization, and continuous integration who can mentor junior engineers and enforce technical best practices.