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Moderate competition: mid-level generalist title and experience, specialized LLM/agent skills narrow applicant pool.
ML/AI engineering skills transferable across industries, but LLM/multi-agent specialization increases domain specificity.
Explicit 4–6 years requirement plus mandatory Python, LLM frameworks, cloud, and MLOps increases strictness.
Design and deliver enterprise-grade AI solutions leveraging LLMs and multi-agent architectures using LangChain and LangGraph.
Own end-to-end development from problem definition to production deployment, including building scalable AI pipelines and APIs.
Optimize AI system performance, cost, and reliability while collaborating with cross-functional product, engineering, and data teams to meet business objectives.
4–6 years of experience in AI/ML engineering or related fields.
Strong Python programming skills required.
Experience with LLMs, LangChain, LangGraph, multi-agent systems, and related frameworks such as MCP.
Experience deploying AI solutions on cloud platforms (Azure/AWS/GCP) and familiarity with MLOps and production deployment practices.
Experienced in architecting and managing complex multi-agent AI systems and workflows.
Proficient in integrating advanced LLM techniques including RAG, embeddings, prompt engineering, and orchestrations.
Comfortable operating in cross-functional, collaborative environments delivering business-driven AI solutions at enterprise scale.