Role & responsibilities

We are seeking an experienced AWS GenAI Engineer with strong expertise in Generative AI, Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), and Amazon Bedrock. The ideal candidate will design, develop, and deploy enterprise-grade AI solutions leveraging AWS cloud services and modern GenAI frameworks to solve business problems at scale.

Preferred candidate profile

Key Responsibilities

- Design and develop GenAI applications using Amazon Bedrock, foundation models, and AWS AI services.
- Build and optimize RAG (Retrieval-Augmented Generation) pipelines for enterprise knowledge systems.
- Integrate vector databases such as OpenSearch, Pinecone, Weaviate, ChromaDB, or FAISS.
- Develop intelligent chatbots, virtual assistants, document Q&A;, and AI-powered search solutions.
- Implement prompt engineering strategies and fine-tuning approaches to improve model performance.
- Create scalable APIs and microservices using Python and AWS serverless technologies.
- Work with LangChain, LlamaIndex, Bedrock Agents, Knowledge Bases, and orchestration frameworks.
- Design data ingestion, embedding generation, indexing, and retrieval workflows.
- Ensure security, governance, and responsible AI practices across solutions.
- Collaborate with business stakeholders, architects, and data engineering teams.
- Monitor model performance, optimize latency, and manage cloud infrastructure costs.

Required Skills

AWS Services

- Amazon Bedrock
- AWS Lambda
- API Gateway
- S3
- IAM
- ECS/EKS
- Step Functions
- CloudWatch
- OpenSearch
- DynamoDB




- Secrets Manager

Generative AI

- Large Language Models (LLMs)
- Prompt Engineering
- RAG Architecture
- Foundation Models (Claude, Llama, Titan, etc.)
- Embeddings and Vector Search
- Fine-Tuning / Model Customization
- AI Agents and Multi-Agent Systems

Programming & Frameworks

- Python (Mandatory)
- LangChain
- LlamaIndex
- FastAPI / Flask
- REST APIs
- Git, CI/CD

Databases

- Vector Databases (Pinecone, OpenSearch, ChromaDB, FAISS)
- SQL / NoSQL Databases

Preferred Qualifications

- Hands-on experience with Amazon Bedrock Knowledge Bases and Bedrock Agents.
- Experience deploying LLM applications in production environments.
- Knowledge of MLOps, Model Monitoring, and AI Governance.
- AWS Certification (AWS Solutions Architect, AWS AI Practitioner, or AWS Machine Learning Specialty) is preferred.
- Experience with Docker, Kubernetes, Terraform, or CloudFormation is an added advantage.

Nice to Have

- Experience with multimodal AI solutions.
- Exposure to OpenAI, Anthropic Claude, or Meta Llama models.
- Knowledge of data pipelines and ETL frameworks.
- Understanding of NLP, embeddings, and semantic search.

Education

- Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, or related field.

Keywords

AWS, Amazon Bedrock, Generative AI, GenAI, RAG, LLM, LangChain, LlamaIndex, Vector Database, Pinecone, OpenSearch, Claude, Knowledge Bases, Bedrock Agents, Python, AI Engineer, Prompt Engineering, Machine Learning, NLP.

Perks and perks

📌 AWS GenAI Engineer @ Noida/Gurgaon / Chennai @ Hybrid mode
🏢 Alight
📍 Noida