Design, build, and deploy enterprise AI and automation solutions on Microsoft Azure and Fabric. Integrate LLMs, Copilot, RAG, vector DBs, and prompt engineering into applications. Build Medallion Architecture data lakes, ETL/ELT pipelines, and Power BI semantic models. Implement governance, security, and responsible AI practices across platforms.
We are seeking an experienced AI Automation Engineer to design, build, and deploy enterprise AI solutions that improve business processes, automate workflows, and enable data-driven decision making. This role combines expertise in artificial intelligence, data engineering, cloud technologies, and automation to develop scalable solutions using the Microsoft AI and Azure ecosystem. The ideal candidate has strong hands-on experience with large language models (LLMs), Microsoft Copilot, Microsoft Fabric, and modern data architectures, along with a passion for applying AI to solve complex business challenges.
Key ResponsibilitiesAI Solutions & Automation- Design, develop, and deploy AI-powered applications and automation solutions that improve operational efficiency and business outcomes.
- Implement Microsoft Copilot solutions and integrate AI models to deliver intelligent insights, natural language querying, and enhanced user experiences.
- Integrate Large Language Models (LLMs) and Generative AI services into business applications using APIs, connectors, and workflow orchestration tools.
- Develop prompt engineering strategies, Retrieval-Augmented Generation (RAG) pipelines, AI agents, vector databases, and tokenization workflows to support enterprise AI initiatives.
- Evaluate emerging AI technologies and recommend innovative solutions that align with business objectives.
- Design and maintain scalable Medallion Architecture data lakes (Bronze, Silver, Gold) within Microsoft Fabric.
- Develop ETL/ELT pipelines using Dataflows Gen2, Fabric Notebooks, PySpark, Python, and Azure Data Factory.
- Build enterprise-grade Power BI semantic models utilizing Direct Lake, DAX, and Kusto Query Language (KQL) to deliver high-performance analytics.
- Optimize data models, pipelines, and query performance to support large-scale reporting and AI workloads.
- Collaborate with business stakeholders to translate data requirements into scalable AI and analytics solutions.
- Implement enterprise-grade governance, security, and compliance standards across Microsoft Fabric and Azure environments.
- Configure Row-Level Security (RLS), workspace governance, access controls, and data protection policies.
- Ensure solutions follow best practices for scalability, reliability, performance, and cost optimization.
- Partner with IT, business leaders, and development teams to establish AI governance and responsible AI practices.
RequirementsEducation & Experience
- Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, or a related field.
- 5+ years of experience in data engineering, AI development, analytics, or cloud platform engineering.
- Experience designing and deploying enterprise AI and automation solutions in Microsoft Azure environments.
- Advanced proficiency in:
- Python
- PySpark
- SQL
- Kusto Query Language (KQL)
- Strong experience with:
- Microsoft Fabric
- OneLake
- Lakehouse
- Data Warehouse
- Fabric Notebooks
- Power BI
- Azure Data Factory
- Delta Lake
- Experience building scalable ETL/ELT pipelines and modern data lake architectures.
- Hands-on experience with:
- Large Language Models (LLMs)
- Microsoft Copilot
- Prompt engineering
- Retrieval-Augmented Generation (RAG)
- AI agent development and orchestration
- Vector databases
- Tokenization strategies
- AI APIs and workflow automation
- Knowledge of enterprise AI architecture, model integration, and responsible AI practices.
- Microsoft certifications such as:
- Microsoft Certified: Fabric Analytics Engineer Associate
- Microsoft Certified: Fabric Data Engineer Associate
- Azure AI Engineer Associate
- Experience with Azure AI Foundry, Azure OpenAI Service, Microsoft Copilot Studio, or similar enterprise AI platforms.
- Familiarity with DevOps, CI/CD pipelines, Git, and Infrastructure as Code.
- Excellent analytical, problem-solving, and communication skills with the ability to collaborate across technical and business teams.
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