Job Title:AI Engineer – Generative AI & Agentic Systems
Location: Noida | Bangalore | Kolkata
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:
- Generative AI: OpenAI, Claude, Gemini, Llama, Mistral, Qwen
- Agentic AI Frameworks: LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Pydantic AI
- RAG & Knowledge Systems: Vector Databases, Knowledge Graphs, GraphRAG
- Programming: Python, FastAPI, REST APIs, Async Programming
- Cloud & AI Platforms: Azure AI Foundry, Azure OpenAI, Vertex AI, AWS Bedrock, Databricks AI
- DevOps & Deployment: Docker, Kubernetes, GitHub Actions, CI/CD
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.
