AI Engineer

Job Locations - Bengaluru/Chennai/Hyerabad/Pune

Looking for Immediate or early joiners only.

Primary Skills

Generative AI, LLMs, RAG, NLP, Prompt Engineering, Agentic AI, Python, REST APIs, LangChain/LangGraph/LlamaIndex, Vector Databases, Gemini Enterprise Agent Platform, GCP (BigQuery, Cloud Functions, Cloud Run), IAM & Responsible AI.

Good to Have Skills:

MLOps/LLMOps, MLflow, Docker, Kubernetes, PyTorch, TensorFlow, GraphRAG, Knowledge Graphs, Databricks, Multimodal AI, Agent Platform Pipelines.

Job Overview:

We are looking for an experienced AI Engineer with experience in designing, developing, and deploying AI-powered applications using Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and NLP.

The ideal candidate should have hands-on experience building enterprise AI solutions using Python, Gemini Enterprise Agent Platform, and GCP services.

Key Responsibilities

• Design and develop AI-powered applications, copilots, chatbots, and knowledge assistants.
• Build and optimize RAG pipelines using embeddings, vector databases, and semantic search.
• Develop NLP solutions for text classification, summarization, entity extraction, and sentiment analysis.
• Implement prompt engineering, AI agents, model evaluation, and responsible AI practices.
• Deploy and manage AI solutions using Gemini Enterprise Agent Platform and GCP services.
• Develop APIs and integrate AI capabilities with enterprise applications.
• Collaborate with Data Engineering and Cloud teams to build scalable AI solutions.

Required Skills

AI & GenAI

• Generative AI (GenAI)
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Natural Language Processing (NLP)
• Prompt Engineering
• AI Agents / Agentic AI
• Fine-tuning and evaluation of LLMs
• Model Context Protocol (MCP)
• Agent Skills
• Grounding with enterprise data
• Tool use / function calling
• Agent orchestration
• Agent monitoring and observability
• IAM, security, and access control for AI agents
• Evaluation datasets and test cases for agents
• Responsible AI governance on GCP

Programming

• Python
• REST API Development
• FastAPI / Flask
• SQL & NoSQL Databases

Frameworks & Tools

• LangChain
• LangGraph
• LlamaIndex
• Vector Databases (Pinecone, ChromaDB, FAISS, Weaviate)

Google Cloud Platform (GCP)

• Gemini Enterprise Agent Platform
• Agent Studio
• Agent Engine
• Model Garden
• Vector Search
• Agent Search
• Agent Evaluation
• Agent Registry
• Agent Gateway
• BigQuery
• Cloud Functions / Cloud Run

Good to Have

• MLOps / LLMOps
• Agent Platform Pipelines
• MLflow
• Docker & Kubernetes
• PyTorch / TensorFlow
• GraphRAG / Knowledge Graphs
• Databricks
• Multimodal AI