Role & responsibilities

**Key Responsibilities**

- Develop AI Agents using AWS Bedrock and agent orchestration frameworks such as LangGraph, CrewAI, or similar.

- Design and implement Retrieval-Augmented Generation (RAG) solutions using vector databases (OpenSearch, LanceDB) and knowledge bases.

- Build and integrate custom tools, APIs, MCP connectors, and external services for AI agents.

- Develop document ingestion pipelines including parsing, OCR, chunking, embeddings, and indexing workflows.

- Integrate AWS Bedrock Guardrails and content safety controls into applications.

- Implement Infrastructure as Code using AWS CDK and JSON-based configurations.

- Optimize prompts, model performance, latency, and operational costs.

- Collaborate with product, architecture,



and support teams for AI solution delivery.

- Monitor application logs, usage metrics, and performance through CloudWatch.

Required Skills

**GenAI**

- LLM concepts, Prompt Engineering

- RAG Architecture

- Agentic AI

- Vector Databases

- Embeddings and Semantic Search

- Model Evaluation and Guardrails

**AWS**

- Bedrock

- Lambda

- Step Functions

- DynamoDB

- S3

- API Gateway

- CloudWatch

- IAM

- Secrets Manager

- AWS CDK

**Development**

- Python (Mandatory)

- REST APIs

- Git/GitHub

- JSON/YAML

- CI/CD pipelines

📌 Gen AI Developer (Mumbai)
🏢 Capgemini
📍 Mumbai