Hiring For AWS and Gen AI/ Agentic AI

NTT DATA, Inc.; Chennai; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

NTT DATA, Inc. in Chennai seeks an experienced AI/ML engineer to architect agentic AI systems and integrate LLMs within enterprise workflows.

You will design multi‑agent orchestration with memory and planning, implement RAG pipelines, and deploy secure cloud solutions on AWS and Azure. Lead teams, mentor peers, and collaborate with product, security, and data engineering to deliver scalable, production‑grade AI solutions.

Qualifications

• :
• "8+ years of Python development and AI/ML experience."
• "Hands-on with LLMs and agent frameworks."
• "Experience integrating enterprise data sources and APIs."
• "Familiarity with cloud services (AWS, Azure) and DevOps practices."
• "Knowledge of RAG, vector databases, and memory/planning in multi-agent systems."
• "Strong testing, CI/CD, and production-grade software engineering."

Responsibilities

• Design and implement agent-based AI systems.
• Develop multi-agent orchestration with memory and planning.
• Integrate LLMs into enterprise workflows.
• Build and optimize RAG pipelines with vector stores.
• Develop backend services and AI microservices using Python.
• Institute secure, scalable cloud architectures and CI/CD pipelines.

Skills

Python

LLMs and agent frameworks

REST APIs

Microservices

Event-driven architecture

CI/CD

Docker

Kubernetes

Git

Vector databases

Cloud services (AWS, Azure)

Tools

AWS

Azure

Pinecone

FAISS

Weaviate

Azure OpenAI

LangChain

LangGraph

AutoGen

Job description

Role & responsibilities

Agentic AI & LLM Development

• Design and implement agent-based AI systems using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar.
• Develop multi-agent orchestration systems with memory, planning, and tool usage capabilities.
• Integrate LLMs (OpenAI, Azure OpenAI, Anthropic, open‑source models) into enterprise workflows.
• Implement RAG (Retrieval‑Augmented Generation) pipelines using vector databases.
• Build secure, production‑grade prompt engineering frameworks.

Python Development

• Develop scalable backend services using Python (FastAPI, Flask, Django).
• Build AI microservices and APIs for enterprise integration.
• Implement async processing, workflow automation, and event‑driven systems.
• Ensure high‑quality code with unit tests, CI/CD integration, and best practices.

Cloud Architecture (AWS & Azure)

• Design and deploy AI solutions on AWS:
  • SageMaker
  • Bedrock
  • Lambda
  • ECS/EKS
  • API Gateway
  • DynamoDB / RDS
• Implement AI workloads on Azure:
  • Azure OpenAI
  • Azure ML
  • Azure Functions
  • Azure Kubernetes Service (AKS)
  • Cognitive Services
• Build secure cloud architectures with IAM, VPC, networking, and encryption best practices.
• Implement infrastructure as code (Terraform, CloudFormation, Bicep).

DevOps & MLOps

• Set up CI/CD pipelines for AI applications.
• Manage containerization using Docker & Kubernetes.
• Implement model monitoring, logging, and performance optimization.
• Ensure governance, compliance, and responsible AI practices.

Leadership & Strategy

• Lead AI architecture discussions and solution design workshops.
• Mentor junior developers and AI engineers.
• Define AI adoption strategy and roadmap.
• Collaborate with cross‑functional teams (Product, Cloud, Security, Data Engineering).

Required Skills & Qualifications

Technical Skills

• Strong expertise in Python (8+ years preferred).
• Hands‑on experience with LLMs and agent frameworks.
• Deep understanding of:
  • Prompt engineering
  • RAG architecture
  • Vector databases (Pinecone, FAISS, Weaviate, Azure AI Search)
  • API integrations
• Experience with AWS and Azure cloud services.
• Strong knowledge of REST APIs, microservices, and event‑driven architecture.
• Experience with Git, CI/CD, Docker, Kubernetes.

Cloud & AI Expertise

• Experience deploying AI systems in production environments.
• Familiarity with AI governance, model evaluation, and monitoring.
• Experience integrating enterprise data sources (SharePoint, SAP, CRM, etc.).