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Tier-1 brand and mid-level generic title increase competition, but niche Agentic AI/LLM skills limit applicant pool.
Specialized Agentic AI, LangGraph, and production LLM requirements reduce cross-industry transferability.
Explicit 5–10 year requirement plus mandatory LLM/LangGraph, cloud, and backend expertise creates high shortlisting strictness.
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Lead design and development of scalable Python backend systems, data pipelines, and Agentic AI/LLM platforms using LangGraph.
Define and drive strategies for LLM system performance, token usage, latency, cost optimization, and AI/RAG architecture standards.
Mentor engineers, resolve complex production issues, and align technical strategy with business goals across teams.
5 to 10 years of hands-on experience in machine learning engineering, backend development, and applied AI.
Deep experience with Agentic AI, LLM applications, LangGraph, RAG architectures, and operationalizing deep learning/LLM models in production.
Strong expertise in Python, backend engineering, distributed systems, and AWS/cloud-native technologies (S3, SQS, SNS, Lambda, Docker, Kubernetes).
Experience with LLM evaluation/observability tooling and optimization of ETL workloads.
Experienced in leading large-scale, cross-functional AI engineering initiatives and mentoring teams.
Strategic thinker able to translate complex AI and backend requirements into scalable, cost-efficient technical solutions.
Skilled in aligning engineering efforts with business objectives and communicating technical concepts to executives.