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Tier-1 brand, metro location, and broad GenAI platform expectations create moderate candidate competition.
Highly specific GenAI, agentic system and MLOps expertise reduces transferability across industries.
Multiple explicit experience thresholds and specialized GenAI, platform, and AWS skills make shortlisting highly strict.
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Own end-to-end architecture, design, and delivery of production-grade agentic and Generative AI systems (on-premise/cloud) ensuring performance, scalability, security, and reliability.
Create reference AI platforms, pipelines, SDKs, and CI/CD templates for the AI Technology Innovation Centre (TIC), translating research into secure, observable, and cost-efficient products.
Lead technical debugging, optimization, evaluation standardization, and cross-functional collaboration to maintain high quality and governed releases of AI solutions.
7–10 years in software/AI engineering including 4+ years in GenAI application development and 2+ years architecting agentic AI systems.
Bachelor’s or Master’s degree from a top-tier institute (IIT or equivalent) in Computer Science, AI, or related field.
Expertise in Python 3.11+ with knowledge of async programming, typing, packaging, and testing frameworks.
Hands-on experience with GenAI frameworks (Semantic Kernel, LangGraph, AutoGen, or CrewAI) and delivery on AWS ecosystem (EKS, Bedrock, S3, SQS/SNS, RDS, ElastiCache, IAM).
Experienced in architecting and delivering scalable, secure GenAI and agentic AI platforms for production environments under compute and reliability constraints.
Strong systems thinker able to lead platform standardization efforts across multiple teams while mentoring developers on best practices in reliability and performance.
Familiar with cloud-native AWS services and MLOps practices, capable of integrating research breakthroughs responsibly into production-grade AI products.