Generative AI Engineer

Netlink Software Group America, Inc.; Gurugram District; On-site; INR 2,500,000 - 3,500,000; Full time

Job summary

Netlink Software Group America, Inc. in Gurugram is seeking a skilled Generative AI Engineer to design, develop, and deploy advanced AI solutions using LLMs, RAG, and agentic workflows.

The role emphasizes building knowledge layers, vector search, embeddings, ontologies and knowledge graphs, with strong Python, cloud, MLOps, and governance practices to deliver production-grade AI.

Qualifications

• Experience with Python and LLM frameworks in building AI products.
• Design knowledge layers, vector databases, embeddings, and semantic search.
• Experience deploying production-grade AI solutions with monitoring and governance.

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.

Skills

Generative AI

LLMs

RAG architecture

Agentic AI

Prompt engineering

Knowledge graphs

Vector search

MLOps

LLMOps

Cloud platforms

Tools

Python

LangChain

LangGraph

LlamaIndex

REST APIs

Docker

Kubernetes

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.