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Tier-1 employer, mid-level title, and sought-after LLM specialization create moderate candidate competition.
Highly specific Agentic AI, LLM, and LangGraph requirements limit cross-industry transferability.
Explicit 5–10 years, mandatory Agentic AI/LLM, LangGraph, and production AWS requirements enforce high shortlisting strictness.
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Lead design and development of scalable Python backend systems and data pipelines aligned with AI, cloud, and backend architectures.
Drive architecture and optimization of complex Agentic AI and LLM systems focusing on performance, latency, token use, and cost.
Mentor engineers, resolve complex production issues, and align technical strategy with business goals through stakeholder partnership.
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 experience operationalizing and deploying deep learning and LLM models in production with strategies for versioning, safeguards, and rollback.
Strong expertise in Python, distributed systems, AWS cloud-native stack (S3, SQS, SNS, Lambda, Docker, Kubernetes), and LLM optimization techniques.
Experienced technical leader capable of mentoring and guiding engineering teams across large-scale, cross-functional projects.
Strategic thinker adept at translating complex AI and backend requirements into scalable solutions and aligning them with business goals.
Strong familiarity with AI/ML operational tooling and architecture standards, including LLM evaluation and observability tools like LangSmith.