Generative AI Engineer

Netlink Computer Inc; Delhi; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Netlink Computer Inc. seeks a skilled Generative AI Engineer to design, develop, and implement AI solutions using LLMs, RAG, and Agentic AI workflows. You will work on unstructured data processing, embeddings, and knowledge graphs to build scalable AI applications.

The role requires hands-on Python experience, familiarity with LangChain/LangGraph, vector databases, and cloud deployment on AWS/Azure, with a focus on production-grade governance and monitoring.

Qualifications

• Hands-on experience with Python and AI frameworks.
• Experience with LLMs, RAG architectures, and agentic AI.
• Familiarity with vector databases and embeddings.
• Knowledge of cloud technologies and deployment pipelines.
• Experience with production-grade AI deployment, monitoring, and governance.

Responsibilities

• Design and develop Generative AI solutions leveraging LLMs, RAG, and Agentic AI.
• Build knowledge layer capabilities including semantic chunking and vector-based search.
• Develop entity extraction pipelines, ontologies, and knowledge graphs.
• Implement agentic workflows, tool calls, and API integrations with enterprise data sources.
• Create scalable AI components, patterns, and MLOps/LMMOps practices.
• Collaborate with cross-functional teams to translate business needs into production AI solutions.

Skills

Generative AI

LLMs

RAG

Agentic AI

Embeddings

Vector search

Entity extraction

Ontologies

Knowledge graphs

Python

APIs

LangChain

LlamaIndex

MCP

REST APIs

AWS

Azure

Docker

Kubernetes

CI/CD

Tools

LangChain

LangGraph

LlamaIndex

MCP

REST APIs

Docker

Kubernetes

CI/CD

AWS

Azure

Job description

Role Overview

We are looking for a skilled Generative AI Engineer to design, develop, and implement Generative AI solutions and Knowledge Layer capabilities using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI workflows. The role involves working on unstructured data processing, semantic chunking, embeddings, vector search, entity extraction, ontology development, and knowledge graphs to build intelligent, scalable, and reliable AI applications.

The ideal candidate should have strong hands‑on experience in Python, LLM frameworks, API integrations, vector databases, and cloud technologies, along with an understanding of production‑grade AI deployment, evaluation, monitoring, and governance.

Key Responsibilities

• Design and develop Generative AI solutions leveraging LLMs, RAG architectures, Agentic AI, and prompt engineering.
• Build knowledge layer capabilities, including unstructured data processing, semantic chunking, embeddings generation, and vector-based semantic search.
• Develop entity extraction pipelines, ontologies, and knowledge graphs to enhance knowledge representation, retrieval, and contextual understanding.
• Design and implement agentic workflows, tool calling, and MCP (Model Context Protocol) / API integrations to connect AI systems with enterprise data sources and external services.
• Develop scalable and reusable AI components, frameworks, and architectural patterns for GenAI and knowledge-driven applications.
• Implement guardrails, model evaluation frameworks, response quality checks, and monitoring mechanisms to ensure accuracy, reliability, and responsible AI usage.
• Build and integrate REST APIs and backend services using Python and relevant frameworks.
• Deploy and manage AI applications using Docker, Kubernetes, CI/CD pipelines, and cloud platforms such as AWS or Azure.
• Implement MLOps and LLMOps practices covering model deployment, versioning, evaluation, monitoring, and continuous improvement.
• Collaborate with cross‑functional teams to translate business requirements into scalable and production‑ready AI solutions.

Technical Skills Required

• Generative AI: Generative AI, Large Language Models (LLMs), prompt engineering, RAG, Agentic AI, AI agents.
• Knowledge Layer: Unstructured data processing, semantic chunking, embeddings, vector search, entity extraction, ontologies, knowledge graphs.
• Frameworks and Integration: Python, LangChain, LangGraph, LlamaIndex or similar frameworks, MCP, REST APIs, API integrations.
• Databases and Search: Vector databases, semantic search, and familiarity with graph databases or knowledge graph technologies.
• AI Quality and Governance: Guardrails, model evaluation, response validation, performance monitoring, and responsible AI practices.
• Cloud and Deployment: AWS and/or Azure, Docker, Kubernetes, CI/CD.
• Operational Practices: MLOps, LLMOps, model deployment, monitoring, and production lifecycle management.

Job Category: Generative AI Engineer

Job Type: Full Time

Job Location: New Delhi Pune Gurgaon

Experience: 4-10 years