AI Engineer/AI Lead (Data Science / Machine Learning | Azure + MCP)
SPARIX GLOBAL PRIVATE LIMITEDPosted on
Experience
10 - 15 yrs
Job Location
Noida, India
Vacancy
1
Designation
Artificial Intelligence Engineer
Job Type
Not specified
Job Description
Expert AI Engineer/AI Lead (Data Science / Machine Learning | Azure + MCP)
Exp: 10+ years
Remote
Overview
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.
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:
10+ years experience in Data Science, ML Engineering, or AI Engineering
Strong Python skills with PyTorch, TensorFlow, scikit-learn, and frameworks such as Lang Chain 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
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.
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:
10+ years experience in Data Science, ML Engineering, or AI Engineering
Strong Python skills with PyTorch, TensorFlow, scikit-learn, and frameworks such as Lang Chain 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
Please share suitable profiles for the requirement.
No Referrers Available
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