Gen AI Engineer

Cognizant; Bengaluru Urban; On-site; INR 3,000,000 - 4,200,000; Full time

Job summary

Cognizant is seeking an AI Engineer / AIML Data Science and Gen AI in Bengaluru to design, develop, and deploy autonomous AI systems. The role requires 6–9 years of experience and strong Python plus Langchain, LangGraph, and LLMs capabilities.

You will build multi-agent workflows, integrate LLMs with APIs and databases, and optimize AI deployments for performance and cost while collaborating with data science, engineering, and product teams.

Qualifications

• 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.

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

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