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Early-mid ML/AI role in a metro with 3–6 year requirement and common LLM demand, moderate competition.
Specialized LLM, agentic AI and vector-database expertise reduces cross-industry transferability.
Explicit 4+ years plus required LLM frameworks, LangChain, vector DB and MLOps skills enforces strict filtering.
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Design and develop autonomous and semi-autonomous AI agent workflows using modern Large Language Model (LLM) frameworks and orchestration systems.
Build custom AI pipelines for enterprises focused on automation, analytics, customer support, document intelligence, and workflow orchestration.
Fine-tune, evaluate, and optimize machine learning and LLM models for accuracy, latency, and cost efficiency; deploy and integrate AI applications on cloud platforms such as AWS, Azure, or GCP.
4+ years of experience in AI/ML engineering or related software engineering roles.
Strong proficiency in Python and modern backend frameworks.
Hands-on experience with LLMs, agentic AI frameworks, RAG systems, prompt engineering, and AI orchestration tools.
Familiarity with REST APIs, microservices, event-driven architectures, CI/CD pipelines, and MLOps; experience using LangChain, LangGraph, LlamaIndex, vector databases, and semantic search systems.
Experienced in building scalable AI workflows with multi-agent systems and memory management components.
Skilled in deploying AI applications on cloud platforms and integrating them with enterprise systems like CRMs and ERPs.
Comfortable with advanced NLP techniques, AI safety protocols, and performance optimization for enterprise-grade AI applications.