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