Lead Consultant – GenAI Engineer

Ready to build the future with AI?

At Genpact, we don’t just keep up with technology—we set the pace. AI and digital innovation are redefining industries, and we’re leading the charge. Genpact’s AI Gigafactory, our industry-first accelerator, is an example of how we’re scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to agentic AI, our breakthrough solutions tackle companies’ most complex challenges. 

If you thrive in a fast-moving, innovation-driven environment, love building and deploying cutting-edge AI solutions, and want to push the boundaries of what’s possible, this is your moment. 

Genpact (NYSE: G) is anadvanced technology services and solutions company that deliverslastingvalue for leading enterprisesglobally.Through ourdeep business knowledge, operational excellence, and cutting-edge solutions – we help companies across industries get ahead and stay ahead.Powered by curiosity, courage, and innovation,our teamsimplementdata, technology, and AItocreate tomorrow, today.Get to know us atgenpact.comand onLinkedIn,X,YouTube, andFacebook. 

Inviting applications for the role ofLead Consultant – GenAI Engineer
We are looking for a highly skilled GenAI Engineer with deep expertise in Agentic AI systems, multiagent orchestration, and the design of communication frameworks for autonomous AI agents. This role involves building productiongrade AI ecosystems that utilize LLM-driven agents, autonomous workflows, retrieval systems, and cloud-native deployments. The ideal candidate has strong experience in Python, LLM orchestration frameworks, and enterprise-grade AI applications.

Responsibilities

1. Agentic AI & MultiAgent System Development

• Design and implement agent-based AI architectures using frameworks like CrewAI, LangChain Agents, AutoGen, or custom agent orchestration layers.

  
  

• Build collaborative multi-agent workflows for reasoning, planning, tool usage, task delegation, and asynchronous communication.

  
  

• Develop protocols for agent-to-agent messaging, memory sharing, context propagation, and feedback loops.
  

  
  

2. AI Application Engineering

• Build Python-based GenAI applications integrating LLMs, vector retrieval, multi-agent pipelines, and autonomous task flows.

  
  

• Implement RAG (Retrieval-Augmented Generation) and Agentic RAG patterns for knowledge-grounded interaction.

  
  

• Develop domain-specific reasoning modules for enterprise or healthcare applications.

  
  

3. LLM Integration & Optimization

• Integrate APIs from OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, etc.

  
  

• Optimize multi-agent reasoning by tuning model parameters (temperature, topp, token windows).

  
  

• Reduce hallucinations using embedding-based retrieval, context injection strategies, and multi-agent verification loops.

  
  

4. Multi-Agent Coordination Frameworks

• Implement agent negotiation, role-based collaboration, and tool-using agents.

  
  

• Build message buses, orchestration layers, or event-driven protocols for inter-agent communication.

  
  

• Develop evaluation methods for agent reliability, consensus scoring, and chainofthought verification.

  
  

5. API & Microservices Development

• Design and maintain scalable RESTful and event-driven microservices for AI workflows.

  
  

• Secure AI endpoints using OAuth2, JWT, API throttling, and gateway routing.

  
  

• Build agent task endpoints,