Microsoft AI Architect

Key Responsibilities

• Develop an enterprise AI strategy aligned with business priorities.
• Define architectural standards, patterns, and best practices for AI, ML, and automation solutions.
• Evaluate emerging AI technologies and recommend adoption where appropriate.
• Architect end-to-end AI solutions, including data ingestion, model development, deployment, and monitoring.
• Design integration patterns between AI services, applications, and existing enterprise systems.
• Collaborate with data engineers, developers, and stakeholders to translate business needs into technical designs.
• Guide teams in building, training, and optimizing machine learning or large-language-model solutions.
• Establish MLOps practices for model lifecycle management, versioning, monitoring, and continuous improvement.
• Ensure AI solutions meet standards for scalability, reliability, and performance.
• Partner with data teams to ensure data availability, quality, and structure for AI workloads.
• Support the design of data pipelines, data lakes, and structured/unstructured data integration.
• Leverage cloud services—such as Azure, AWS, or GCP—to implement scalable compute and storage architectures.
• Establish guidelines for the responsible and ethical use of AI.
• Support governance practices, including model documentation, validation, and risk mitigation.
• Ensure compliance with security, privacy, and regulatory requirements.
• Serve as a subject-matter expert and advisor to business units evaluating AI use cases.
• Lead technical design reviews, proof-of-concept projects, and platform evaluations.
• Mentor engineering teams on best practices in AI, automation, and modern software architecture.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
• 8+ years of experience in AI/ML engineering, data engineering, cloud architecture, or software engineering.
• Experience with at least one major cloud AI ecosystem (Azure, AWS, or GCP).
• Familiarity with Microsoft technologies such as: Foundry, Azure AI Services or Azure Machine Learning, Power Platform AI capabilities, Microsoft 365 or Copilot integration concepts.
• Strong understanding of data pipelines, APIs, vector search, and modern application integration.

Preferred Qualifications

• Experience designing AI solutions for enterprise environments.
• Microsoft, AWS, or GCP certifications.
• Understanding of LLM architectures, prompt engineering, or retrieval-augmented generation (RAG).
• Experience in model monitoring, drift detection, and MLOps tooling.