AWS AI/ML Engineer (GenAI, Agents, MCP & RAG)

Role & responsibilities

Job Title: AWS AI/ML Engineer (GenAI, Agents, MCP & RAG)

Location:India

Experience: 6+ Years

Employment Type: Full-Time

Preferred candidate profile

Job Summary

We are looking for an experienced AWS AI/ML Engineer with expertise in Generative AI, Large Language Models (LLMs), AI Agents, Model Context Protocol (MCP), and Retrieval-Augmented Generation (RAG). The ideal candidate will design, develop, and deploy enterprise-scale AI solutions on AWS, leveraging foundation models, agentic AI frameworks, vector databases, and MLOps best practices. This role requires strong software engineering skills combined with hands-on experience in building production-grade GenAI applications.

Key Responsibilities

Design and develop Generative AI applications using AWS AI/ML services.

Build and deploy RAG-based solutions using vector databases and enterprise data sources.

Develop AI agents capable of multi-step reasoning, tool usage, and workflow orchestration.

Implement MCP integrations to connect LLMs with enterprise tools, APIs, and data systems.

Fine-tune, evaluate, and optimize foundation models for business use cases.

Design scalable and secure AI architectures on AWS.

Develop prompt engineering, guardrails, and responsible AI frameworks.

Implement MLOps and LLMOps practices for model deployment and monitoring.

Collaborate with data engineers, architects, and business stakeholders to deliver AI-driven solutions.

Monitor AI application performance, cost, and model quality in production.

Required Skills

AI/ML & Generative AI

Strong experience with Generative AI and Large Language Models (LLMs).

Hands-on experience building RAG (Retrieval-Augmented Generation) solutions.

Experience with AI Agents, autonomous workflows, and tool-calling frameworks.

Knowledge of Model Context Protocol (MCP) and AI-agent integrations.

Expertise in prompt engineering and LLM evaluation techniques.

Experience with embeddings, semantic search, and vector databases.

AWS Services

Amazon Bedrock

SageMaker

Lambda

API Gateway

ECS/EKS

Step Functions

DynamoDB

S3

OpenSearch

CloudWatch

IAM

Programming

Python

LangChain

LangGraph

LlamaIndex

FastAPI

REST APIs

Data & Vector Databases

Pinecone

Weaviate

Chroma

FAISS

OpenSearch Vector Engine

PostgreSQL (pgvector)

MLOps / LLMOps

CI/CD for AI applications

Model deployment and monitoring

MLflow

Docker

Kubernetes

GitHub Actions / Jenkins

Terraform

Preferred Skills

Experience with OpenAI, Anthropic Claude, Meta Llama, or Mistral models.

Experience developing enterprise AI copilots and conversational assistants.

Knowledge of multimodal AI (text, image, audio, video).

Experience with Responsible AI, AI governance, and security frameworks.

AWS Certified Machine Learning Engineer or AWS Certified AI Practitioner certification.

Experience working with healthcare, banking, insurance, or other regulated industries.

Mandatory Skills

AWS

Amazon Bedrock

Python

Generative AI

LLMs

RAG

AI Agents

MCP (Model Context Protocol)

LangChain / LangGraph

Vector Databases

SageMaker

MLOps

Keywords for Sourcing

AWS AI Engineer, GenAI Engineer, Generative AI Engineer, LLM Engineer, AI/ML Engineer, Amazon Bedrock, SageMaker, RAG, Retrieval Augmented Generation, AI Agents, Agentic AI, MCP, Model Context Protocol, LangChain, LangGraph, LlamaIndex, Vector Database, Pinecone, OpenSearch, Python, Prompt Engineering, LLMOps, MLOps, Semantic Search, Embeddings, AI Copilot, Foundation Models

Nice-to-Have Skills

CrewAI

AutoGen

Semantic Kernel

OpenAI APIs

Anthropic Claude

Hugging Face

Fine-tuning

Knowledge Graphs

GraphRAG

Multi-Agent Systems

Kubernetes

Terraform

Serverless AI Architectures

Experience Range: 6-12+ Years

Target Profiles: GenAI Engineer, LLM Engineer, AI Platform Engineer, AI Solutions Engineer, AI Architect, Applied AI Engineer, Machine Learning Engineer (GenAI Focus).