Roles and Responsibilities
- Design and deliver enterprise-grade AI/ML solutions across multi-cloud environments (Azure, AWS), ensuring scalability, security, performance, and seamless integration with existing technology ecosystems.
- Define and enforce comprehensive AI governance frameworks addressing compliance (e.g., GDPR, EU AI Act), model risk, ethics, transparency, and explainability.
- Serve as a trusted advisor to business and technology leaders on AI strategy, emerging trends, and the long-term impact of AI on enterprise processes, platforms, and decision-making.
- Lead end-to-end development and operationalization of AI solutions, including data exploration, model development, training, validation, deployment, and lifecycle management.
- Own and manage production operations of AI systems-covering monitoring, incident management, CI/CD pipelines, and release/change controls.
- Assess and evaluate change requests, effort, and impact across business functions, technology platforms, and governance controls to ensure strategic alignment and risk mitigation.
- Drive continuous improvement in model performance, system reliability, and AI delivery processes through rigorous testing, automation, and adherence to engineering best practices.
- Partner with cross-functional teams (data engineering, application development, infrastructure, business units) to translate complex business challenges into actionable AIdriven solutions.
- Champion a culture of technical excellence and knowledge sharing by mentoring peers, reviewing code and architecture, and contributing to internal AI communities of practice.
Required Skills and Experience
- Bachelor's or master's degree in computer science, data science, information technology, or a related field, with 5+ years of experience in both AI/ML architecture and hands-on machine learning development.
- Strong foundation in machine learning and familiarity with deep learning techniques and frameworks.
- Demonstrated experience with MLOps and model lifecycle practices (training pipelines, monitoring, retraining, and CI/CD for ML).
- In-depth understanding of AI governance and responsible AI practices, including bias detection, model explainability, and alignment with global regulatory standards.
- Proven ability to design and scale AI solutions using cloud-native services across Azure (Azure Machine Learning, Azure OpenAI, Cognitive Services) and AWS.
- Proficient in Python and its ML ecosystem experience integrating models into enterprise environments using APIs, containers, or microservices architectures.
- Proficient in Microsoft Power Platform components (e.g., Copilot Studio, Power Apps, Power Automate, Power BI, Dataverse) for embedding AI into low-code/no-code environments.
- Experience working with Microsoft development tools and stacks: Visual Studio, C#/.NET, JavaScript/TypeScript, SQL Server.
- Relevant certifications in Azure or AWS AI/ML tracks are strongly preferred.
- Strong communication and stakeholder management skills, with the ability to translate complex AI concepts into clear business insights and actionable roadmaps.
- Highly self-driven and accountable, with a demonstrated ability to lead in fast-paced, matrixed enterprise environments.
