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Tier-1 employer and metro location with in-demand GenAI skills create moderate competition.
Requires specialized GenAI production and MLOps expertise, limiting cross-industry transferability.
Explicit 8–12 years plus mandatory GenAI production experience and AWS/tech stack enforces strict filters.
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Develop, build, and deploy enterprise-grade Generative AI applications on AWS, including RAG pipelines, agentic workflows, and AI-powered assistants.
Own end-to-end lifecycle of GenAI solutions from prototyping to production: architecture, deployment, monitoring, and optimization with enterprise-grade security and responsible AI practices.
Collaborate directly with business teams to understand workflows and deliver impactful AI solutions, building reusable frameworks to scale adoption across teams.
8–12 years of professional software engineering experience.
At least 2 years of production experience with Generative AI / LLM-based systems.
Strong backend engineering skills in Python, with expertise in APIs, microservices, distributed systems, and AWS services including Amazon Bedrock, S3, Lambda, ECS/EKS, API Gateway, OpenSearch/vector search, RDS/Postgres, IAM, KMS, Secrets Manager, and CloudWatch.
Experience building secure, scalable GenAI systems with RAG, embeddings, vector databases, agent/tool calling, and orchestration.
Hands-on builder focused on solving real business problems with AI through production-grade code and end-to-end ownership.
Comfortable working in ambiguous environments and collaborating closely with cross-functional business and product teams in a forward-deployed engineering model.
Experienced in architecting and scaling enterprise GenAI applications within AWS cloud ecosystems and integrating responsible AI practices.