Job Description
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, vector search APIs, and orchestration services.
6. Cloud Deployment & MLOps
Deploy agent-based AI systems on AWS, Azure, or Kubernetes.
Use Docker, CI/CD pipelines, and cloud orchestration for scalable model deployment.
Manage observability, logging, monitoring, and autoscaling for multi-agent workloads.
7. Agile Delivery & Innovation
Work within Scrum teams to deliver incremental AI capabilities and autonomous features.
Contribute to architecture design reviews, POCs, and solution accelerators for enterprise AI adoption.
Qualifications we seek in you!
Minimum Qualifications / Skill
Experience in AI/ML, multi-agent systems, or LLM-based solutions.
Strong expertise in Agentic AI design, LLM orchestration, autonomous workflows, and RAG architecture.
Hands-on experience building scalable, secure, cloud-native AI platforms.
Excellent understanding of vector retrieval, embeddings, and model evaluation.
AI/ML, Enterprise Applications, Healthcare
Problem-solving attitude
Team spirit