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Tier-1 brand and metro location increase competition, but niche Agentic AI/LLM specialization reduces applicant pool.
Role requires specialized LLM and Agentic AI production expertise, making cross-industry transferability low.
Explicit 5–10 year requirement plus mandatory LLM, LangGraph, and production experience makes filters highly strict.
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Lead design and development of scalable Python backend systems and data pipelines focused on Agentic AI, Large Language Models (LLM), and LangGraph.
Drive architecture and optimization strategies for LLM performance, token management, latency, and cost across AWS and cloud-native platforms.
Mentor engineers, lead cross-team resolution of complex issues, and align technical strategy with business goals.
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, and Retrieval-Augmented Generation (RAG) architectures.
Proven operational experience deploying LLM models and LangGraph with safeguards, versioning, and rollback strategies.
Strong expertise in Python, backend engineering, distributed systems, and AWS technologies including S3, SQS, SNS, Lambda, Docker, and Kubernetes.
Experienced in leading large-scale, cross-functional engineering initiatives and mentoring teams in advanced AI/backend domains.
Strategic thinker capable of translating complex AI requirements into scalable, production-ready technical architectures.
Skilled in integrating AI tool ecosystems and aligning technical strategy tightly with business and stakeholder priorities.