AI Engineer

We are seeking an experienced AI Engineer with a strong background in Data Science and Machine

Learning, and hands-on experience building and operating AI solutions on Microsoft Azure, including Azure AI Foundry, Azure OpenAI, Fabric, and Azure MCP (Model Context Protocol). This role blends advanced ML/GenAI development with production-grade engineering and cloud architecture to deliver scalable, secure, enterprise AI systems.

Required Experience: 5+yrs

Job Location:Chennai/Pune

Work Mode:Hybrid

If interested please share an updated profile to

Kavitha Subramani

Talent Acquisition Analyst

kavitha@simpliigence

(+91) 74839 25904

Key Responsibilities

Build, fine-tune, and deploy ML and GenAI models (predictive models, NLP, embeddings, RAG

pipelines, LLM-powered agents).Design end-to-end AI architectures using Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure ML/Fabric, Azure Functions, App Services, and AKS.

Design and operate MCP servers to expose enterprise tools, data sources, and workflows to AI

agents.Implement secure MCP connectors for knowledge bases, databases/APIs, and internal services with RBAC and auditability.Partner with data engineers to build feature stores, training pipelines, and inference pipelines using Fabric/ADLS/Databricks.Implement CI/CD for models and prompts, monitoring, drift detection, retraining pipelines, and versioning.Implement security and governance using RBAC, Managed Identities, Azure Key Vault, logging, and data lineage.Collaborate with Product, UX, Platform, and Business stakeholders to deliver production-grade AI systems.

Required Qualifications

5+ years experience in Data Science, ML Engineering, or AI Engineering,Strong Python skills with PyTorch, TensorFlow, scikit-learn, and frameworks such as LangChain or

Semantic Kernel.Hands-on experience with Azure OpenAI, Azure AI Search, Azure ML/Fabric, Azure Functions or AKS.Experience building RAG architectures, AI agents, and AI APIs.Experience deploying ML/AI to production with monitoring, scaling, and reliability practices.Strong understanding of the ML lifecycle, feature engineering, prompt engineering, and vector

databases.