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Tier-1 brand, mid-level generalist title, and popular LLM/backend skills driving high candidate density.
Highly specialized LLM, LangGraph, and production ML skills limit cross-industry transferability.
Explicit 5+ years and mandatory LLM, LangGraph, and productionization skills create highly strict shortlisting.
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Lead design and development of scalable Python backend systems, data pipelines, and Agentic AI/LLM systems using LangGraph.
Define and implement strategies for LLM performance, token usage, latency, and cost optimization, and establish standards for AI/RAG architecture and distributed systems.
Mentor engineers, lead resolution of complex technical and 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, and RAG architectures, including operationalizing deep learning and LLM models in production.
Strong expertise in Python, backend engineering, distributed systems, AWS cloud-native technologies like S3, SQS, SNS, Lambda, Docker, and Kubernetes.
Experience with LLM evaluation and observability tooling (e.g., LangSmith) and defining token, latency, throughput, and cost optimization strategies for LLM systems.
Experienced technical leader capable of mentoring engineers and driving large-scale, cross-functional AI/LLM platform initiatives.
Strategic thinker skilled at translating complex AI requirements into scalable, performant, and cost-effective technical solutions.
Strong communicator able to align technical strategy with business goals and deliver executive-level technical storytelling.