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Tier-1 brand and metro location increase competition, but niche GenAI seniority limits applicant pool.
Specialized LLM production, RAG, and enterprise AWS requirements make the role strongly domain-specific.
Explicit 8–12 years, mandatory GenAI production experience, and AWS/backend requirements enforce strict filters.
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Develop and productionize enterprise-grade Generative AI applications on AWS, including RAG pipelines, agentic workflows, and AI-powered assistants.
Own end-to-end lifecycle of GenAI systems from prototype to production encompassing architecture, deployment, monitoring, and optimization.
Collaborate directly with business teams to identify workflows and deliver impactful AI solutions while ensuring enterprise-grade security and responsible AI practices.
8–12 years of professional software engineering experience.
Minimum 2+ years of hands-on production experience with Generative AI or LLM-powered systems.
Strong backend engineering skills in Python including APIs, microservices, and distributed systems.
Proficiency with AWS cloud services such as Amazon Bedrock, Lambda, ECS/EKS, S3, API Gateway, OpenSearch/vector search, and security components like IAM and KMS.
Experienced in delivering production-grade GenAI solutions using RAG, multi-agent systems, embeddings, and vector databases in enterprise environments.
Comfortable working in forward-deployed engineering roles partnering directly with business stakeholders to solve complex, ambiguous problems end-to-end.
Skilled at building scalable, secure, and maintainable AI systems with a strong emphasis on responsible AI and operational excellence.