AI Engineer (Noida)

Polestar Analytics; Dadri; On-site; INR 1,500,000 - 2,200,000; Full time

Job summary

Polestar Analytics in Noida is seeking an AI Engineer specializing in Generative AI and Agentic Systems. The role focuses on designing, developing, and deploying enterprise-grade AI solutions powered by LLMs and foundation models.

You will build RAG systems, multi‑agent architectures, and orchestration frameworks for scalable AI platforms. The ideal candidate has 3–8 years of experience, strong Python and FastAPI skills, and a proven track record of production-grade AI deployments in enterprise

Qualifications

• 3–8 years of experience in AI/ML or software engineering with focus on Generative AI.
• Hands-on experience building applications using LLMs/foundation models.
• Strong experience with Retrieval-Augmented Generation (RAG) systems and vector databases.
• Experience designing conversational AI, enterprise copilots, and intelligent assistants.

Responsibilities

• Design, develop, and deploy enterprise-grade AI apps powered by LLMs.
• Build RAG solutions leveraging vector databases and knowledge repositories.
• Develop conversational AI assistants and workflow automation solutions.
• Implement prompt engineering, CoT, Multi-Step Reasoning, and tool calling.

Skills

Python

FastAPI

REST APIs

Asynchronous programming

LLMs

RAG

Multi-agent AI

Agentic AI

Education

Bachelor's or Master's in CS/AI

Tools

LangGraph

LangChain

CrewAI

AutoGen

Semantic Kernel

Pydantic AI

Job description

Job Title: AI Engineer – Generative AI & Agentic Systems

Location: Noida

Employment Type: Full-time (5 Days/week from office)

Experience: 3–8 Years

Industry Focus: IT Services, Artificial Intelligence & Analytics

Position Summary

We are looking for a highly skilled AI Engineer – Generative AI & Agentic Systems with 3–8 years of experience in designing, developing, and deploying enterprise-grade AI solutions powered by Large Language Models (LLMs). The ideal candidate will have hands‑on expertise in Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), multi‑agent architectures, and AI orchestration frameworks. This role involves building intelligent AI assistants, enterprise copilots, autonomous workflows, and scalable AI platforms that drive business innovation and operational efficiency.

Strategic Responsibilities

• Design, develop, and deploy enterprise-grade applications powered by Large Language Models (LLMs) and foundation models.
• Build Retrieval-Augmented Generation (RAG) solutions leveraging vector databases, enterprise knowledge repositories, and GraphRAG architectures.
• Develop conversational AI assistants, enterprise copilots, intelligent chatbots, and workflow automation solutions.
• Implement advanced prompt engineering techniques including Chain of Thought (CoT), ReAct, Self-Reflection, Multi-Step Reasoning, and Tool Calling.
• Design and develop multi-agent AI systems capable of autonomous planning, reasoning, task decomposition, and execution.
• Build agent orchestration frameworks integrating enterprise applications, APIs, and external services.
• Implement agent memory management, communication protocols, and human-in-the-loop workflows.
• Develop scalable AI services, APIs, and reusable AI platform components for enterprise deployments.
• Integrate AI solutions with enterprise platforms including CRM, ERP, Supply Chain, Data Warehouses, and Knowledge Management systems.
• Design and implement AI governance, monitoring, observability, and evaluation frameworks.
• Evaluate AI applications for relevance, groundedness, hallucination, safety, accuracy, and overall performance.
• Build automated evaluation pipelines, testing frameworks, and continuous model validation processes.
• Optimize inference performance, latency, scalability, token utilization, and infrastructure costs.
• Collaborate with Product, Engineering, Data Science, and Business teams to deliver production-ready AI solutions.
• Stay updated with emerging trends in Generative AI, Agentic AI, LLMs, and enterprise AI platforms.

Required Experience

• 3–8 years of experience in Artificial Intelligence, Machine Learning, or Software Engineering with a strong focus on Generative AI.
• Hands‑on experience building applications using Large Language Models (LLMs) and foundation models.
• Strong experience developing Retrieval-Augmented Generation (RAG) systems using vector databases and enterprise knowledge repositories.
• Experience designing and deploying conversational AI applications, enterprise copilots, and intelligent assistants.
• Hands‑on experience implementing multi‑agent AI systems and autonomous workflows.
• Strong proficiency in Python, FastAPI, REST APIs, and asynchronous programming.
• Experience with AI orchestration frameworks and enterprise AI integrations.
• Strong understanding of prompt engineering, model evaluation, and production deployment best practices.
• Experience developing scalable AI APIs and enterprise‑grade AI applications.

Technical Skills

• Agentic AI Frameworks: LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Pydantic AI
• RAG & Knowledge Systems: Vector Databases, Knowledge Graphs, GraphRAG

Good to Have

• Experience building enterprise‑grade AI copilots and digital assistants.
• Experience deploying multi‑agent AI systems in production environments.
• Knowledge of AI governance, observability, monitoring, and responsible AI practices.
• Understanding of Model Context Protocol (MCP) and modern AI interoperability standards.
• Experience developing domain‑specific AI solutions for Retail, CPG, Banking, Telecom, Healthcare, or Manufacturing.
• Familiarity with MLOps, model lifecycle management, and cloud-native AI deployments.

Educational Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.

Soft Skills

• Strong analytical and problem‑solving skills.
• Excellent communication and collaboration abilities.
• Ability to work effectively in cross-functional and agile teams.
• Strong ownership mindset with a focus on delivering scalable AI solutions.
• Passion for innovation and continuous learning in emerging AI technologies.
• Ability to manage multiple priorities in a fast‑paced environment.
• Detail‑oriented with a strong focus on quality, performance, and business impact.