Software Development Specialist

NTT DATA, Inc.; Chennai; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

NTT DATA, Inc. seeks a senior Agentic AI Lead/Developer to design, develop, and deploy autonomous agent systems in enterprise environments.

You will combine hands-on coding in Python with architectural leadership, integrating LLMs across AWS and Azure along with modern AI frameworks. You will lead multi-agent projects, drive scalable cloud-native solutions, and establish best practices for governance, testing, and production readiness.

Qualifications

• 8+ years of Python and AI development experience.
• Hands-on experience with LLMs and agent frameworks.
• Deep understanding of prompt engineering, RAG, and vector databases.
• Evidence of production AI deployments and governance practices.

Responsibilities

• Design agent-based AI systems and scalable multi-agent orchestration.
• Integrate LLMs (OpenAI, Azure OpenAI, Anthropic) into enterprise workflows.
• Implement RAG pipelines using vector databases.
• Build secure, production-grade prompt engineering frameworks.

Skills

Python

LLMs

Agent frameworks

Cloud architectures

Leadership

CI/CD

DevOps

MLOps

Tools

LangChain

LangGraph

AutoGen

CrewAI

Semantic Kernel

REST APIs

Docker

Kubernetes

SageMaker

Azure OpenAI

Job description

We are seeking a highly skilled Agentic AI Senior Lead / Developer to design, develop, and deploy advanced AI systems leveraging autonomous agents, LLMs, and cloud-native architectures. The ideal candidate will have strong expertise in Python, AWS, and Azure, and experience building scalable AI-driven solutions using modern AI frameworks and cloud services.

This role combines hands-on development, architecture design, and technical leadership in delivering intelligent, autonomous, and multi-agent systems for enterprise use cases.

Key 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.).

• Implement RAG (Retrieval-Augmented Generation) pipelines using vector databases.
• Prompt engineering