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Location
In office (Hyderabad)
Seniority
Staff

Confirmed on the employer's own hiring board on Nov 5, 2026. First seen by Alion on Nov 5, 2026.

Overview
Company
Impact
Profile match
Tech Mahindra is a prominent Indian multinational information technology services and consulting enterprise specializing in digital transformation, cloud modernization, business process re-engineering, and network services. Headquartered in Pune, Maharashtra, India (with its registered office in Mumbai), the company is a flagship entity of the diversified Mahindra Group conglomerate. Established in 1986 (initially as Mahindra British Telecom), it operates across dozens of countries with a global delivery footprint serving enterprise clients in telecommunications, banking and financial services, healthcare, automotive, and retail. The organization is particularly recognized for its deep engineering heritage in telecommunications and 5G network integration, offering end-to-end solutions spanning artificial intelligence, enterprise application management, customer experience (CX), and next-generation connectivity infrastructure.
Backed by Seafund

Job Description

  • Skill Set :

    Generative AI

  • Total Experience :

    7.00 to 10.00 Years

  • No of Openings :

    1

  • Job Post Date :

    04/11/2026

  • Job Expiry Date :

    30/11/2026

  • Domain :

    IT

  • Location :

    HYDERABAD [India]

  • Job Reference No :

    4116561

Job Summary

Mandatory Skills

Python, ML, MLOps, Langchain, LangGraph, Data Bricks, DevOps

Build, deploy, and enhance machine learning and GenAI solutions aligned with validated user requirements.

Develop and maintain AI agents using Python, Azure services, APIs, LLM frameworks, retrieval patterns, and appropriate interface technologies.

Use AI coding assistants and LLM tooling to plan, scaffold, refactor, test, document, and debug code, while validating outputs before adoption.

Create agent-based automations for data-quality investigation, failure triage, documentation, schema and lineage analysis, dataset discovery, and routine remediation.

Test, refine, and govern prompts; identify new prompts aligned with evolving business needs and approved solution scope.

Incorporate user feedback, agent metrics, and production outcomes into a closed-loop improvement process for prompts, models, and agent behaviour.

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