Gen AI Engineer

Cognizant; Bengaluru; On-site; INR 2,600,000 - 4,200,000; Full time

Job summary

Cognizant is hiring an AI Engineer / AIML Data Science and Gen AI to design, build, and deploy agentic AI systems. The role focuses on autonomous agents, multi-agent orchestration, and seamless tool usage. You will integrate LLMs with enterprise APIs and data sources, implementing RAG pipelines for scalable AI services.

The position requires 6–9 years of experience, Bangalore location, and an immediate to 90 days notice. Virtual or in-person interviews may be conducted.

Qualifications

• Proven experience designing and deploying AI agents.
• Experience with multi-agent orchestration and tool usage.
• Strong Python skills and API integration.
• Familiarity with Retrieval-Augmented Generation (RAG).

Responsibilities

• Design, develop, and deploy Agentic AI systems using Python.
• Build autonomous AI agents capable of planning, reasoning, and tool usage.
• Develop and manage multi-agent workflows and orchestration pipelines.
• Integrate LLMs with APIs, databases, and enterprise applications.
• Implement Retrieval-Augmented Generation (RAG) pipelines.
• Develop scalable backend services and microservices for AI applications.
• Optimize performance, latency, and cost of AI systems in production.
• Collaborate with Data Science, Engineering, and Product teams.
• Ensure best practices in testing, monitoring, and deployment of AI systems.

Skills

AI Engineer

Python

Langchain

LangGraph

Agentic AI

API

LLMs

Job description

Role: AI Engineer / AIML Data Science and Gen AI

Experience : 6 to 9 Years

Location : Bangalore

Notice Period : Immediate to 90 days

Mode of Interview : Virtual / In-Person

Mandatory skills: AI Engineer Python, Langchain, LangGraph, Agentic AI, API, LLMs

Role & responsibilities

• Design, develop, and deploy Agentic AI systems using Python
• Build autonomous AI agents capable of planning, reasoning, and tool usage
• Develop and manage multi-agent workflows and orchestration pipelines
• Integrate LLMs with APIs, databases, and enterprise applications
• Implement Retrieval-Augmented Generation (RAG) pipelines
• Develop scalable backend services and microservices for AI applications
• Optimize performance, latency, and cost of AI systems in production
• Collaborate with Data Science, Engineering, and Product teams
• Ensure best practices in testing, monitoring, and deployment of AI systems