Principal Azure Cloud AI Engineer

Principal Azure Cloud AI Engineer

Requirements

• Deep understanding of cloud providers Azure, especially: Experience with AI and GenAI-related cloud services, especially: Azure ML. This should include common commercial Foundational Models (FM) from OpenAI, Anthropic, etc, as well as open-source LLM models deployed in the cloud.
• GenAI LLM Platform Experience: Model Evaluation, Model API patterns and implementations, Model Governance, Retrieval Augmented Generation, Orchestration, Prompt Libraries, Agents, Tools/Functions, Prompt/Tuning, Chunking Methods, Vector Stores/DBs/Embeddings, Foundational LLM Models (commercial and open source), Model Hubs, Fine-Tuned Models
• AI-related Data Platforms (i.e. DataBricks, IBM watsonx, or similar)
• GenAI Code Assistants and Developer Experience (i.e. GitHub Copilot, AWS Code Whisperer, etc.)
• Deep AI/ML experience with data science, data analytics, etc.
• Solid understanding of cloud security in highly regulated market segments and countries.
• Solid experience with site reliability engineering mindset and creating solutions that are resilient, supportable and observable at all layers of the stack
• Deep understanding of automation using various tools
• Deep understanding of observability in a cloud environment
• Proficient in web service design, standards, best practices and implementation
• Deep understanding of containerization and designing ephemeral solutions
• Solid understanding of pure Kubernetes and cloud provider based managed services Kubernetes
• Proven track record of delivering solutions to complex, multi-domain environments
• Ability to articulate complex designs and solutions to people with varying levels of technical aptitude
• Experienced in guiding less experienced engineers with the use of pair programming, code reviews, design reviews, etc.
• Deep knowledge in migration from legacy technologies and mindset to the best in class solutions for the cloud
• Self-Driven and able to navigate complex organizational environments
• Strong communication skills both written and verbal
• Strong understanding of different project management methodologies including waterfall and Agile/Scrum
• Strong understanding of all phases of the SDLC process from design to deployment
• Enthusiastically engages engineers across Technology organizations to promote standard software patterns and reuse of common libraries and services with experience leading open-source development efforts
• Champions performance engineering practices to ensure that performance meets (or exceeds) expectations; educates stakeholders on performance testing processes, methodology, performance and scalability metrics, capacity modeling techniques, and testing approaches
• Understands software development productivity metrics (e.g., code churn, commit size, commits/story) and helps teams to remove blockers and continuously improve code velocity, quality, and release frequency
• Experienced with Python, Java, and other programming languages.

Responsibilities

• Design, configure, and implement a Gen. AI platform, including MLOps/LLMOps for Gen. AI LLMs, both commercial and open source
• Ensure alignment to appropriate patterns and standards for cloud integration and automation
• Identify opportunities for reuse and improved efficiency
• Engage with IT and Business partners, product owners and stakeholders to create meaningful roadmaps to ensure the most important work is prioritized
• Champion all Mastercards engineering principles
• Actively participate as a member of the Software Engineering Guild sharing your knowledge, best practices, ideas, and passion for technology
• Help identify and drive meaningful behavior changing metrics

Preferred Qualifications

No preferred qualifications provided.