Agentic AI Or Generative AI- Lead Engineer

Wipro; Hyderabad, Gurugram District, Bengaluru; Hybrid; INR 3,500,000 - 7,500,000; Full time

Job summary

Wipro is seeking a senior technical leader with deep hands-on expertise in Agentic AI systems and Generative AI. The role emphasizes production-grade AI, scalable architectures, and enterprise patterns across AI platforms. Strong background in distributed systems and platform engineering is essential.

The ideal candidate will contribute to design decisions, drive production deployments, and collaborate across teams to scale AI capabilities across enterprise use cases.

Qualifications

• 10+ years of relevant software engineering / technology experience.
• Proven experience as Lead Developer, Technical Lead, Senior Engineer, or equivalent hands-on leadership role.
• Hands-on experience designing and developing Agentic AI systems.
• Experience taking AI agents and Generative AI solutions into production.
• Ability to operate at a senior technical level: design, architecture, decisions, troubleshooting, production readiness.
• Experience in large-scale distributed systems is strongly preferred.
• Experience in platform/architecture/lead engineer or similar senior roles.

Skills

Lead Developer

Technical Leadership

Senior Engineer

Agentic AI systems

AI production deployment

Distributed systems design

Tools

OpenAI GPT

Google Gemini

Llama/enterprise LLMs

Job description

Experience Requirements

• 10+ years of relevant software engineering / technology experience.
• Proven experience working as a Lead Developer, Technical Lead, Senior Engineer, or equivalent hands-on technical leadership role.
• Strong hands-on experience designing and developing Agentic AI systems.
• Demonstrated experience taking AI agents and Generative AI solutions into production.
• Ability to independently operate at a senior technical level, including solution design, architecture, engineering decisions, troubleshooting, and production readiness.
• Experience designing, developing, and operating large-scale distributed systems is strongly preferred.
• Experience in platform, architecture, lead engineer, or similar senior technical roles is preferred.

Core Technical Requirements

Agentic AI

Candidate must demonstrate practical, hands-on experience with:

• Designing and developing AI agents / Agentic AI systems.
• Building AI agents that have been deployed and operated in production environments.
• Developing Agentic AI solutions beyond prototypes, proof-of-concepts, or basic chatbot implementations.
• Designing reusable Agentic AI capabilities that can support enterprise applications and multiple use cases.

Generative AI / LLM Engineering

Strong hands-on experience developing Generative AI solutions using Large Language Models (LLMs) such as:

• OpenAI GPT
• Google Gemini
• Llama or comparable enterprise LLMs

Experience should include practical development and integration of Generative AI capabilities into enterprise applications and production systems.

AI Platform & Architecture

Strong understanding and hands-on experience with:

• AI platform architecture
• API design
• Distributed systems
• Reliability engineering
• Building reusable platform components that can be consumed by multiple teams rather than developing only one-off AI applications.
• Designing scalable and maintainable enterprise AI capabilities.

Enterprise Agentic AI Patterns

Experience with enterprise AI/ML or GenAI platform capabilities, including areas such as:

• Tool and agent registry patterns
• Private registries
• Retrieval and grounding patterns
• Context management
• Memory patterns
• Context budgeting
• Production hardening
• Reusable reference implementations
• Accelerators/frameworks that enable adoption across multiple teams

AI Quality, Evaluation & Observability

Experience implementing foundations for:

• AI quality measurement
• AI/LLM evaluation practices
• AI telemetry
• Monitoring
• Reliability
• Production observability

Candidates should understand how AI solutions are evaluated and monitored after deployment, not only how models or agents are developed.

MLOps / Production Engineering

Exposure to or hands-on experience with:

• Cloud ML tooling
• Enterprise MLOps practices
• Production deployment of AI/ML solutions
• Operational monitoring and reliability
• Production hardening of Generative AI / Agentic AI solutions