Senior Consultant Data

Microsoft Microsoft · Big Tech · IN · Technology Consulting

Senior Consultant Data & AI at Microsoft, focusing on delivering end-to-end data and AI solutions for clients. Responsibilities include designing, developing, and deploying solutions on Microsoft technologies, building AI-powered data pipelines, feature engineering, operationalizing ML pipelines, and working with LLMs, NLP, Computer Vision, RAG, and Gen AI frameworks. The role emphasizes Azure Data Services, Data Warehouse, and MLOps concepts.

What you'd actually do

  1. Works as an Individual contributor and key member of the Data and AI team and helps in timely execution of assigned deliverables with accurate estimates, work priorities, and accommodates project changes and trade-offs necessary for a successful release.
  2. Applies technical experience and industry-specific knowledge to develop solutions, based on an analysis of how the proposed approach affects the business objectives of customers and partners.
  3. Works to accelerate the value proposition of customer/partner engagements by helping to design, develop, and deploy solutions on Microsoft technologies and methodologies.
  4. Contributes to the overall efficacy and quality of a project team’s technical delivery within assigned engagements.
  5. Defines dependencies and risks that go beyond the immediate scope and timeframe for a complex project. Develops contingency plans, risk-mitigation implementation criteria, and alternative strategies to manage short- and long-term risks and manages technical escalations.

Skills

Required

  • 10+ years of experience
  • Bachelor's degree in computer science engineering or equivalent work experience
  • Knowledge of solution design, planning, development and deployment of complex solutions
  • Hands-on experience in Data Engineering across cloud, on-prem, and hybrid environments
  • Strong foundational experience with Azure Data Services and data platform modernization initiatives
  • Handson exposure to Data Warehouse and analytics using platforms like Microsoft Fabric, and Azure Synapse Analytics
  • Experience/knowledge of one or more SQL and NoSQL database systems
  • Hands-on experience building AI-powered data pipelines using ETL/ELT tools like: Azure Data Factory (ADF), SSIS, Talend, Informatica, Airflow
  • Exposure to data migrations, platform upgrades, and modernization efforts
  • Understanding of multitenant data platform designs, basic security hardening, and access control concepts
  • Knowledge of Big Data ecosystems like Spark, Databricks, Kafka, Hadoop, etc.
  • Experience building or supporting ML ready datasets and basic feature engineering
  • Exposure to operationalizing ML pipelines and MLOps concepts, on Azure stack
  • Foundational knowledge of: Large Language Models (LLMs), Model training, evaluation, and deployment workflows, Azure cloud and AI stack like Azure Foundry, Azure OpenAI, etc
  • Handson experience or knowledge in one or more of: NLP, Document Intelligence & Indexing, Computer Vision, RAG frameworks or AI Search
  • Basic understanding of Prompt Engineering
  • Exposure to or knowledge of deep learning/Gen AI frameworks such as TensorFlow/PyTorch, and orchestration frameworks like LangChain/LangGraph
  • Familiarity with Responsible AI principles – fairness, interpretability, and governance
  • Experience working in Agile teams with knowledge of/exposure to Azure DevOps
  • Understanding of DataOps and MLOps practices, automated testing, CI/CD pipelines, and deployment workflows, preferably on Azure Foundry
  • Knowledge of secure coding practices, observability basics, and performance co

Nice to have

  • Higher relevant education is preferred
  • Microsoft Certified: Azure Data Engineer Associate (DP-600) / Microsoft Certified: Azure AI Engineer Associate (AI102) / Microsoft Certified: Azure Solution Architect Expert (AZ-305)
  • Azure Databricks is a plus

What the JD emphasized

  • 10+ years of experience
  • Hands-on experience in Data Engineering across cloud, on-prem, and hybrid environments
  • Strong foundational experience with Azure Data Services and data platform modernization initiatives
  • Handson exposure to Data Warehouse and analytics using platforms like Microsoft Fabric, and Azure Synapse Analytics
  • Hands-on experience building AI-powered data pipelines using ETL/ELT tools like:
  • Experience building or supporting ML ready datasets and basic feature engineering
  • Exposure to operationalizing ML pipelines and MLOps concepts, on Azure stack
  • Foundational knowledge of:
  • Large Language Models (LLMs)
  • Model training, evaluation, and deployment workflows
  • Azure cloud and AI stack like Azure Foundry, Azure OpenAI, etc
  • Handson experience or knowledge in one or more of:
  • NLP, Document Intelligence & Indexing
  • Computer Vision
  • RAG frameworks or AI Search
  • Basic understanding of Prompt Engineering
  • Exposure to or knowledge of deep learning/Gen AI frameworks such as TensorFlow/PyTorch, and orchestration frameworks like LangChain/LangGraph
  • Familiarity with Responsible AI principles – fairness, interpretability, and governance

Other signals

  • delivers end-to-end solutions
  • accelerated adoption and productive use of Microsoft technologies
  • delivers quality engagements with expertise
  • key contributor to high profile data and AI projects
  • technical skills, leadership skills, creativity, and customer focus
  • design, develop, and deploy solutions on Microsoft technologies
  • implementing the technology strategy
  • Azure Data and AI
  • Data Engineering across cloud, on-prem, and hybrid environments
  • Azure Data Services
  • Data Warehouse and analytics using platforms like Microsoft Fabric, and Azure Synapse Analytics
  • building AI-powered data pipelines using ETL/ELT tools
  • ML ready datasets and basic feature engineering
  • operationalizing ML pipelines and MLOps concepts, on Azure stack
  • Large Language Models (LLMs)
  • Model training, evaluation, and deployment workflows
  • Azure cloud and AI stack like Azure Foundry, Azure OpenAI
  • NLP, Document Intelligence & Indexing
  • Computer Vision
  • RAG frameworks or AI Search
  • Prompt Engineering
  • deep learning/Gen AI frameworks such as TensorFlow/PyTorch
  • orchestration frameworks like LangChain/LangGraph
  • Responsible AI principles