Solution Architecture and Deployment:

Build REST APIs for AI/ML models and deploy them in containerised environments (Docker, Kubernetes) on cloud platforms (AWS, Azure, GCP).

GenAI And LLM Development

Apply techniques like Retrieval-Augmented Generation (RAG) and prompt engineering to enhance model performance and relevance.

Work with both commercial and open-source LLMs (e. g., Agentic AI Integration

Build, deploy, and optimise AI agents leveraging frameworks such as LangChain, LangGraph, CrewAI, AgentFlow, and Autogen.

MLOps And Performance Optimisation

Establish MLOps pipelines covering model lifecycle: training, CI/CD, monitoring, and retraining.

Use tools like Git, Docker, Kubernetes, and vector DBs to ensure efficient and reliable deployment.

Partner with engineering, data science, and product teams to align technical solutions with business goals.

Effectively communicate complex concepts across diverse technical and non-technical audiences.

Stay current with industry advancements and drive innovation in GenAI and AI agent strategy.

Robust proficiency in Python, SQL, and GenAI frameworks (e. g.,



Hands-on experience in building and deploying AI agents with orchestration, tool use, and state management.

In-depth knowledge of LLM architecture, RAGs, embeddings, prompt tuning, vector databases, agentic AI patterns (ReAct, tool-calling agents, multi-step reasoning, guardrails)

Experience with cloud platforms (AWS, Azure, GCP) and containerisation.

Data integration experience with REST APIs, Google APIs, and SQL databases. Comfortable moving data between systems.

Experience in Web development: FastAPIs, Typescript, async patterns, building production APIs, React, node.js, Component architecture, hooks, state management, consuming streaming APIs (SSE/WebSocket)

3+ years of hands-on experience with LLMs and GenAI in production settings.

- Exposure to agentic AI tools and multi-agent workflows (e. g., Familiarity with MLOps and AI deployment best practices.
- Experience in client-facing or cross-functional AI initiatives.
- Publications, open-source contributions, or demonstrable projects showcasing AI agent development.

📌 AI / ML Specialist (Bengaluru)
🏢 Nineleaps
📍 Bengaluru