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Tier-1 brand, mid-level generalist title, and hybrid metro role drive high applicant competition.
Highly specialized Agentic AI/LLM, LangGraph, and production model operationalization needs make background fit highly sensitive.
Many mandatory technical requirements and specific LLM/infra experience imply 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 implement performance, token, latency, and cost optimization strategies for LLM systems in production environments.
Drive AWS and cloud-native architecture, resolve complex technical issues, mentor engineers, and align technical strategy with business goals.
5 to 10 years of experience in machine learning engineering, backend development, and applied AI.
Deep experience with Agentic AI, LLM applications, LangGraph, and RAG architectures.
Proven ability to operationalize deep learning and LLM models with deployment experience including versioned prompts and rollback strategies.
Strong expertise in Python, distributed systems, AWS services (S3, SQS, SNS, Lambda, Docker, Kubernetes), and LLM optimization (token, latency, throughput, cost).
Experienced leader capable of mentoring engineers and influencing cross-functional technical initiatives.
Strategic thinker able to translate complex AI and backend requirements into scalable solutions aligned with business priorities.
Expert in advanced AI tool ecosystems, multi-agent systems, and enterprise-scale distributed architectures with strong communication skills.