Back
Tier-1 employer plus metro location increase competition, moderated by niche GenAI specialization.
Production LLM, RAG, embeddings, and GenAI ops requirements reduce cross-industry transferability.
Explicit 6–9 years, 2+ years GenAI, and mandatory AWS/LLM stack create strict screening filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
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 solutions from prototyping to production: architecture, deployment, monitoring, and optimization.
Collaborate directly with business teams to translate workflows into scalable, secure, and responsible AI solutions, building reusable frameworks and evaluation pipelines.
6–9 years of professional software engineering experience.
At least 2 years of production experience with Generative AI / LLM-based systems.
Strong backend expertise in Python with experience in APIs, microservices, and distributed systems.
Hands-on experience with AWS cloud services (Amazon Bedrock, S3, Lambda, ECS/EKS, API Gateway, OpenSearch/vector search, RDS/Postgres, IAM, KMS, Secrets Manager, CloudWatch) for building GenAI applications.
Proven track record of building and scaling production-grade GenAI systems with measurable impact across enterprise workflows.
Experience working in forward-deployed or customer-facing engineering roles capable of managing ambiguous business problems and delivering robust technical solutions.
Strong focus on security, responsible AI practices, and developing reusable tools to accelerate GenAI adoption within enterprises.