About the Role:

We are seeking a highly skilled Engineer – AI to design, develop, and deploy enterprise-grade Artificial Intelligence and Generative AI solutions that solve complex business challenges. This role combines expertise in LLMs, Machine Learning, AI Agents, RAG architectures, prompt engineering, and cloud-native AI platforms to build scalable, production-ready AI applications.

You will collaborate with Solution Architects, Data Engineers, ML Engineers, Product Owners, and business stakeholders to build intelligent applications, AI copilots, conversational agents, and enterprise automation solutions. The ideal candidate is passionate about emerging AI technologies and enjoys building creative solutions that create measurable business impact.

Key Responsibilities

Generative AI Solution Development

- Design and develop enterprise-grade Generative AI applications using Large Language Models (LLMs).
- Build AI-powered copilots, virtual assistants, intelligent chatbots, and enterprise AI agents.
- Develop Retrieval-Augmented Generation (RAG) solutions using vector databases and enterprise knowledge sources.
- Implement prompt engineering strategies to improve response quality, accuracy, and business relevance.
- Fine-tune, evaluate, and optimize AI models for enterprise use cases.

AI Engineering & Machine Learning

- Develop scalable AI and Machine Learning pipelines for training, inference, and deployment.
- Build intelligent workflows integrating LLMs with enterprise applications and APIs.
- Develop semantic search, document intelligence, recommendation engines, and knowledge retrieval systems.
- Implement model evaluation, monitoring, observability, and continuous improvement practices.
- Optimize AI solutions for performance, scalability, security, and cost efficiency.

AI Platform & Cloud Engineering





- Develop AI applications using Azure AI Services, Azure OpenAI, OpenAI APIs, Databricks, Microsoft Fabric, AWS Bedrock, or equivalent platforms.
- Build scalable AI microservices and REST APIs for enterprise integration.
- Integrate AI solutions with cloud-native architectures and enterprise ecosystems.
- Deploy AI applications using CI/CD pipelines and MLOps best practices.
- Ensure responsible AI implementation, governance, and compliance.

Data & Knowledge Engineering

- Develop data pipelines supporting AI model training and inference.
- Build vector embeddings, semantic indexes, and enterprise knowledge repositories.
- Integrate structured, semi-structured, and unstructured enterprise data into AI workflows.
- Design scalable document processing and information extraction pipelines.
- Ensure data quality and governance across AI solutions.

Delivery & Collaboration

- Participate in solution architecture, technical design, and client workshops.
- Work closely with cross-functional teams to translate business requirements into AI solutions.
- Perform code reviews, testing, debugging, and production support.
- Mentor junior engineers and contribute to reusable AI accelerators and frameworks.
- Stay current with emerging AI technologies and recommend innovation opportunities.

Roles and Responsibilities

Ideal Candidate Profile

- 4–6 years of experience in Artificial Intelligence, Machine Learning, Data Science, or AI Engineering.




- Strong hands-on experience with Generative AI and enterprise AI application development.
- Experience working with:
- Azure OpenAI Service
- OpenAI APIs
- Azure AI Foundry
- Azure AI Search
- Databricks
- Microsoft Fabric
- AWS Bedrock (preferred)

- Strong expertise in:

- Python
- SQL
- REST APIs
- Git

- Hands-on experience with:

- LangChain
- LangGraph
- LlamaIndex
- Semantic Kernel
- AutoGen or equivalent AI Agent frameworks

- Experience implementing:

- Retrieval-Augmented Generation (RAG)
- AI Agents
- Prompt Engineering
- Vector Databases (FAISS, Pinecone, ChromaDB, Azure AI Search)
- Embeddings and Semantic Search

- Experience with Machine Learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Familiarity with Docker, Kubernetes, Azure DevOps, CI/CD, and MLOps practices.
- Understanding of Responsible AI, AI Governance, Security, and Model Evaluation.
- Excellent analytical, communication, and problem-solving skills.
- Experience working in consulting or enterprise client-facing environments.

Preferred Qualifications

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related discipline.
- Microsoft AI Engineer Associate (AI-102) Certification preferred.
- Azure Data Engineer (DP-203), Azure Fabric (DP-600/DP-700), or Databricks certifications are an advantage.
- Experience building:
- Enterprise AI Copilots
- Multi-Agent AI Systems
- AI-powered Document Intelligence Solutions
- Intelligent Process Automation
- AI-driven Analytics Platforms

- Familiarity with MCP (Model Context Protocol), Agentic AI, and emerging AI frameworks.
- Experience deploying AI applications in Azure or AWS cloud environments.

📌 Engineer - AI (Mumbai)
🏢 Exponentia.AI Private
📍 Mumbai