Back
Tier-1 brand, metro hiring, mid-senior experience but niche Agentic LLM specialization reduces broad applicant pool.
Highly specialised LLM and LangGraph production expertise limits cross-industry transferability.
Explicit 5–10 years, mandatory LLM/Agentic AI, LangGraph, Python, and cloud production experience.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead design and development of scalable Python backend systems and data pipelines focused on Agentic AI/LLM using LangGraph.
Drive architecture, performance, and cost optimization strategies for LLM systems including token, latency, and throughput.
Mentor engineers, resolve complex production issues, define AI/RAG architecture standards, 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 RAG architectures, including operationalizing deep learning and LLM models in production.
Strong expertise in Python, backend engineering, distributed systems, and AWS/cloud-native services like S3, SQS, SNS, Lambda, Docker, Kubernetes.
Experience with LLM evaluation and observability tooling and implementing optimization strategies for token usage, latency, throughput, and cost.
Experienced leader capable of mentoring engineers and driving large-scale, cross-functional AI software initiatives.
Strategic thinker able to translate complex AI and backend requirements into scalable technical solutions aligned with business goals.
Strong communicator proficient in technical storytelling and integration of AI tool ecosystems like Model Context Protocol.