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Tier-1 brand, mid-senior ML role, metro location, and broad AI skill requirements increase candidate competition.
Core LLM and ML engineering skills transfer across industries, but finance-specific governance raises sensitivity modestly.
Requires advanced ML skills, specific LLM frameworks, cloud and DevOps expertise, and leadership, so filters will be strict.
Design and implement LLM-driven AI agent services for code generation, documentation, testing, and observability on AWS.
Develop orchestration and communication layers between AI agents using frameworks like A2A SDK, LangGraph, or Auto Gen.
Lead technical guidance and drive adoption of AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes.
Degree: BE/B.Tech, ME/MS, or PhD in Computer Science or Machine Learning related field.
Strong hands-on experience in Python, Pydantic, FastAPI, LangGraph, Vector Databases, and deploying AI agent solutions on AWS (EKS, Lambda, S3, Terraform).
Experience integrating Large Language Models (LLMs) and working with AI agent frameworks such as Langchain, LangGraph, Autogen, MCPs, A2A.
Deep knowledge in data structures, algorithms, machine learning, and expertise in at least one domain: NLP, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking/Recommendation, or Time Series Analysis.
Experienced in designing and delivering complex AI-driven agentic systems integrating multiple AI frameworks and cloud deployments.
Strong technical leadership capabilities in AI engineering making measurable improvements in software development lifecycle automation.
Deep specialization in machine learning and AI with proven ability to integrate LLMs and agent orchestration frameworks in cloud-native environments.