Key Responsibilities

- Design and develop scalable backend applications using Python and modern frameworks such as FastAPI.

- Build and deploy production-grade Generative AI and LLM applications.

- Develop Retrieval-Augmented Generation (RAG) pipelines for enterprise AI use cases.

- Integrate Amazon Bedrock foundation models into applications and AI workflows.

- Implement LangChain/LangGraph based LLM workflows and AI applications.

- Work with embeddings, semantic search and vector databases.

- Design and implement microservices and serverless architectures using AWS.

- Develop solutions using AWS services such as S3, Lambda, API Gateway, RDS and Step Functions.

- Build and manage containerized applications using Docker and Kubernetes.

- Work with Red Hat OpenShift Service on AWS (ROSA) where required.

- Design and maintain CI/CD pipelines using Jenkins, GitLab or AWS CodePipeline.

- Troubleshoot, optimize and maintain cloud-native applications across development and production environments.

- Implement secure and scalable AI applications using appropriate IAM, networking and cloud security practices.

- Apply prompt engineering, model integration, response validation and AI safety techniques.

- Explore and implement contemporary AI capabilities including AI Agents, AWS AgentCore and Model Context Protocol (MCP).

- Leverage AI-assisted development tools such as Kiro, GitHub Copilot and Claude to improve coding, testing, debugging and documentation.

- Collaborate with engineering,



product and AI/ML teams to deliver production-quality solutions.

Mandatory Skills

- 4+ years of software development / SDE experience

- Strong Python programming

- Generative AI / GenAI

- LLM / Large Language Models

- RAG / Retrieval-Augmented Generation

- Amazon AWS Bedrock

- LangChain

- REST APIs / FastAPI

- AWS cloud services

- Microservices / Serverless architecture

- Docker

- Kubernetes

- Git / Version Control

- CI/CD

Good to Have

- AWS AgentCore

- Model Context Protocol (MCP)

- LangGraph

- AI Agents / Agentic AI

- Kiro

- GitHub Copilot

- Claude

- AWS S3, Lambda, API Gateway, RDS, Step Functions

- CloudFormation

- Jenkins / GitLab CI / AWS CodePipeline

- Red Hat OpenShift / ROSA

- Vector databases such as Pinecone, FAISS, Chroma

- Embeddings and semantic search

- AWS IAM, networking and security

- AWS Certified Solutions Architect / Developer

Candidate Profile

- Strong backend software engineering mindset with hands-on development experience.

- Proven experience building production GenAI/LLM applications, not just POCs or theoretical AI projects.

- Strong understanding of RAG architecture, LLM integration and prompt engineering.

- Experience deploying applications on AWS and working with cloud-native architectures.

- Strong debugging, analytical and problem-solving skills.

- Ability to write clean, maintainable and production-quality code.

- Good communication and collaboration skills.

- Bachelor's degree in Computer Science, Engineering, Information Systems or a related discipline.

📌 SDE AI Engineer Generative AI AWS Bedrock RAG Python (Bengaluru)
🏢 Quess
📍 Bengaluru