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Tier-1 employer and mid-level seniority increase competition; niche agentic AI specialization reduces pool.
Specialized agentic AI and LLM expertise is transferable but enterprise MLOps expectations increase domain sensitivity.
Multiple mandatory years, specific agentic AI/LLM expertise and cloud/MLOps stack create strict filtering.
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Lead design, development, and deployment of large-scale agentic AI solutions using Google ADK, LangChain, and LangGraph frameworks.
Architect and implement multi-agent systems integrating LLMs (OpenAI, Anthropic, Google Gemini) and AI capabilities (Vertex AI, RAG pipelines, vector databases).
Develop scalable Python backend services and drive CI/CD, automated testing, MLOps, and containerization on cloud platforms (AWS, Azure, GCP).
5+ years in AI/ML development, applications development, or systems analysis with at least 2 years focused on AI, prompt engineering, or agentic AI systems.
Proven experience as lead developer for agentic flow design using Google ADK.
Advanced Python skills including FastAPI, asyncio, and scalable backend development.
Experience with LLMs (OpenAI GPT, Gemini, Claude, Llama), vector databases (Pinecone, Weaviate), cloud platforms (AWS, Azure, GCP), Docker, Kubernetes, CI/CD, and secure REST API design.
Technical leader skilled in architecting complex multi-agent AI systems and autonomous agents with multi-step reasoning.
Experienced in integrating large language models and AI tools into scalable, resilient backend microservices.
Comfortable driving DevOps, MLOps, automated testing, and mentoring engineers in a cloud-native, Agile environment.