Key Responsibilities

Agentic AI Integration

- Build, deploy, and optimize AI agents using frameworks such as LangChain, LangGraph, CrewAI, AgentFlow, and AutoGen.

- Design and implement agent orchestration strategies for complex business workflows.

- Develop multi-agent systems with collaboration, planning, and task coordination capabilities.

- Integrate external tools, APIs, databases, and enterprise systems into AI agents.

- Implement memory and state management for long-running, context-aware agents.

- Design autonomous and semi-autonomous AI solutions to automate business processes and support decision-making.

- Evaluate and experiment with emerging Agentic AI frameworks, architectures, and best practices.

MLOps & Performance Optimization

- Design and maintain end-to-end MLOps pipelines for model training, deployment, monitoring, CI/CD, and retraining.

- Deploy and manage AI applications using Git, Docker, Kubernetes, and cloud platforms (AWS/Azure/GCP).

- Optimize AI model performance, latency, scalability, and infrastructure costs.

- Implement monitoring, logging, versioning, and observability for production AI systems.

- Utilize vector databases for efficient retrieval and RAG-based applications.

Cross-Functional Collaboration

- Collaborate with engineering, data science, product, and business teams to deliver AI-driven solutions.

- Translate business requirements into scalable AI architectures.

- Communicate complex AI concepts effectively to both technical and non-technical stakeholders.

- Stay up to date with the latest advancements in Generative AI, Agentic AI, and MLOps technologies.





- Drive innovation by identifying opportunities to improve AI capabilities and business outcomes.

Required Skills & Qualifications

- Strong proficiency in Python and SQL.

- Hands-on experience with LangChain and other Generative AI frameworks.

- Experience building and deploying production-ready AI agents.

- Expertise in agent orchestration, tool integration, memory management, and state management.

- Strong understanding of LLMs, RAG, embeddings, prompt engineering, and vector databases.

- Experience implementing Agentic AI patterns such as ReAct, tool-calling, multi-step reasoning, planning, and guardrails.

- Experience with cloud platforms including AWS, Azure, or GCP.

- Hands-on experience with Docker, Kubernetes, and containerized deployments.

- Experience integrating enterprise applications through REST APIs, Google APIs, and SQL databases.

- Experience developing AI applications using FastAPI.

- Knowledge of TypeScript, React, and Node.js.

- Experience with asynchronous programming and streaming APIs (SSE/WebSockets).

- Solid analytical, problem-solving, and communication skills.

Preferred Qualifications

- 4+ years of experience developing and deploying LLM and Generative AI solutions in production.

- Experience with Agentic AI frameworks including CrewAI, LangGraph, AutoGen, and AgentFlow.

- Strong understanding of MLOps practices and AI deployment best practices.

- Experience delivering AI solutions in client-facing or cross-functional environments.

- Contributions to open-source projects, technical publications, research, or demonstrable AI agent development projects.

📌 Senior AI Engineer Agentic AI & Generative AI (Bengaluru)
🏢 WNS Holdings
📍 Bengaluru