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Tier-1 brand, mid-level generalist title, and metro posting increase applicant competition.
Highly specialized Agentic AI, LLM, LangGraph and production ML requirements limit cross-industry transferability.
Explicit 5–10 years and deep, mandatory LLM/Agentic AI and LangGraph expertise restrict candidate pool.
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Lead design and development of scalable Python backend systems, data pipelines, and complex Agentic AI/LLM systems using LangGraph.
Define and implement strategies for LLM performance optimization including token management, latency reduction, and cost control.
Drive cloud-native architecture (AWS services like S3, SQS, SNS, Lambda, Docker, Kubernetes), resolve complex production 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.
Strong expertise in Python, backend engineering, distributed systems, and Agentic AI/LLM applications including LangGraph and RAG architectures.
Proven experience operationalizing deep learning and LLM models in production with multi-agent workloads, versioned prompts, and rollback strategies.
Experience with LLM evaluation and observability tools (e.g., LangSmith) and ETL optimization techniques.
Experienced in leading large-scale, cross-functional AI initiatives with ability to translate complex requirements into scalable solutions.
Strategic thinker capable of aligning technical architecture and engineering standards with business priorities and stakeholder needs.
Familiar with enterprise platform integration and AI tool ecosystems (e.g., Model Context Protocol) with strong communication skills including executive-level technical storytelling.