Role Overview :

We are looking for an experienced AI Engineer with expertise in Agentic AI and Generative AI to design, develop, and deploy production-grade AI solutions on the AWS ecosystem. The ideal candidate will have strong experience in building LLM-powered applications, AI agents, orchestration frameworks, RAG pipelines, and scalable cloud-native solutions.

You will collaborate with Data Engineering, UI, and DevOps teams to build intelligent AI systems capable of automation, reasoning, and enterprise-scale integrations.

Key Responsibilities :

- Design and develop Agentic AI and Generative AI solutions using Python and AWS.

- Build production-ready AI agents with orchestration, tool calling, workflow automation, and state management.

- Develop scalable Retrieval-Augmented Generation (RAG) pipelines with grounded responses and structured outputs.

- Integrate LLM applications with enterprise databases, APIs, analytics platforms, and UI applications.

- Implement robust workflows with retry mechanisms, error handling, observability, logging, and monitoring.

- Optimize AI applications for latency, scalability, reliability, and cost.

- Collaborate with cross-functional teams including Data Engineering, UI Development, DevOps, and Product teams.

- Follow coding standards, testing practices, documentation, and deployment best practices.

Tech Stack & Skills :

1. Programming : Python, modular code, unit testing.

2. Generative AI & LLM Engineering : Prompt Engineering, LLMs, Structured Outputs, LLM Evaluation, Grounding, AI Safety & Guardrails.

3. Agentic AI : AI Agents, Orchestration, Planner & Router Patterns, Tool Calling, Fan-out/Fan-in, State Management, Multi-Agent Systems.

4. AWS Cloud : Lambda, API Gateway, Step Functions, S3, DynamoDB, IAM, CloudWatch, Bedrock, Foundation Models.

5. Database : Redshift, Aurora, SQL.

6. Production Engineering : Error Handling, Idempotency, Correlation IDs, Logging, Monitoring, Observability.

Preferred Skills : Amazon Bedrock Knowledge Bases, OpenSearch, RAG, Citation-based AI, Indexing, Transcribe, Comprehend, SageMaker, Model Registry, QuickSight, CodePipeline, GitHub Actions, Infrastructure as Code (CDK/Terraform), Performance/Cost Optimization, Pharma/Healthcare domain experience.

Soft Skills : Strong ownership, excellent communication, technical architecture clarity, cross-functional collaboration, quality-focused mindset.