Gen AI Engineer

Cognizant • Science & Engineering • IT Outsourcing & Dedicated Teams • Python • 4 days ago
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Salary
≈ $17k – $37k per year (Estimated)
Location
Remote (India)
Seniority
Senior · 6+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Sep 18, 2026. Cognizant scores B on the Alion truth index.

Overview
Company
Impact
Profile match

Cognizant

Cognizant is an American information technology services and consulting company founded in 1994 as an in-house technology unit of Dun and Bradstreet in Chennai, India, and headquartered in Teaneck, New Jersey. It builds, integrates and operates software and infrastructure for large enterprises, with particularly deep positions in healthcare, financial services, insurance and life sciences, delivered by a global workforce of more than three hundred thousand people concentrated in India. Listed on Nasdaq, the company competes with the large Indian and global systems integrators and has reoriented its offerings around cloud migration, data platforms and AI-assisted engineering.
cognizant.com • HQ: Teaneck, United States • Cloud Consulting & Migration • Engineering Services • IT Outsourcing & Dedicated Teams • IT Consulting & Digital Transformation • Systems Integrators • 5000+ employees • Est. 1994
Tech stack of Cognizant
Backed by Peak XV Partners

Role: AI Engineer / AIML Data Science and Gen AI

Experience : 6 to 10 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
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