Azure DevOps Lead - AI Delivery
Overview
You will own the path from business problem to shipped, adopted AI product. As the single point of accountability between business stakeholders and the engineering team, you will shape what gets built, define what 'done' means, and ensure delivered solutions produce measurable value. You will work with a team of engineers and data scientists, collaborating with business units across the organization. This role stands out for its blend of technical credibility and commercial fluency, allowing you to challenge engineering decisions with authority.
What You'll Do8
- 1Define the AI product roadmap and prioritize initiatives based on business impact and technical feasibility.
- 2Translate business requirements into technical specifications and solution architecture using Azure AI and DevOps practices.
- 3Lead the engineering team in designing, building, and deploying AI solutions, ensuring alignment with Azure best practices.
- 4Own the end-to-end delivery lifecycle from ideation to production, managing risks, timelines, and stakeholder expectations.
- 5Drive the adoption of AI solutions by measuring business outcomes and iterating on feedback.
- 6Facilitate communication between business and technical teams, translating complex concepts into clear terms.
- 7Implement continuous integration and continuous deployment (CI/CD) pipelines using Azure DevOps to automate workflows.
- 8Monitor and optimize AI models in production, ensuring performance and reliability.
Requirements8
- 15+ years of experience in AI/ML solution delivery, with at least 2 years in a lead role.
- 2Proven track record of architecting and deploying AI solutions on Azure, including Azure Machine Learning and Azure Cognitive Services.
- 3Strong expertise in Azure DevOps for CI/CD and infrastructure as code using Terraform or ARM templates.
- 4Hands-on experience with Python and SQL for data manipulation and model development.
- 5Excellent stakeholder management skills, with the ability to influence at all levels.
- 6Deep understanding of MLOps practices and model lifecycle management.
- 7Experience with containerization using Docker and orchestration with Kubernetes.
- 8Strong analytical and problem-solving skills, with a data-driven approach to decision-making.
Salary Insight
Salary not disclosed in listing
Location
Required Skills
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