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Mid-level, metro role and popular AI title increase competition, while niche Agentic AI skills reduce it.
Highly specialized Agentic AI, LLM, and LangChain expertise limits cross-industry transferability.
Explicit 6–8 years requirement and mandatory specialized LLM, Agentic AI, and deployment skills make shortlisting strict.
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Design, develop, and deploy autonomous AI agents and multi-step AI workflows using Agentic AI frameworks and LLMs.
Build and manage RAG pipelines with vector databases and integrate AI agents with enterprise systems and APIs.
Deploy and maintain scalable AI services with Python on cloud platforms using Docker, Kubernetes, ensuring production observability, security, and optimization.
6–8 years of software engineering / AI/ML experience.
Strong hands-on expertise in Python and Agentic AI, including frameworks like LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel.
Experience with large language models (LLMs), RAG pipelines, embeddings, vector databases (e.g., FAISS, Pinecone), and cloud platforms (Azure, AWS, or GCP).
Experience with Docker, Kubernetes, CI/CD, production deployment, REST APIs, and enterprise system integrations.
Experienced in building and scaling autonomous AI systems with multi-agent and hierarchical architectures.
Proficient in deployed AI solutions focusing on accuracy, latency, cost, security, and observability in enterprise environments.
Comfortable working with cloud-native services and complex integrations across SaaS platforms and business applications.