The Core Responsibilities For The Job Include The Following

Solution Architecture and Deployment

- Design and deploy scalable, secure GenAI architectures integrated into customer-facing products.

- 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

- Fine-tune and optimise generative models, including GPT, VAEs, GANs, and transformer-based architectures.

- 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., GPT-4 Claude, LLaMA 3.2 Phi).

Agentic AI Integration

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

- Implement orchestration strategies, multi-agent collaboration, tool integration, and memory/state management.

- Drive experimentation to create autonomous or semi-autonomous agents that solve real business workflows and decision-making processes.

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 effective and reliable deployment.

- Optimise resource utilisation and infrastructure costs.

Cross-Functional Collaboration

- 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.

Requirements

- Strong proficiency in Python, SQL, and GenAI frameworks (e. g., LangChain).

- 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.

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

- 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)

Preferred

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

- Exposure to agentic AI tools and multi-agent workflows (e. g., CrewAI, LangGraph, and Autogen).

- 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.

This job was posted by Ana Bardhan from Nineleaps.

📌 AI Engineer (Bengaluru)
🏢 Nineleaps
📍 Bengaluru