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About The Position
The Artificial Intelligence (AI) Practitioner will be responsible for designing, developing, implementing and optimizing AI-driven solutions that improve efficiency and productivity by reducing manual effort. This role requires demonstrated expertise and a strong track record in leading design, development, and implementation of AI solutions, with a clear emphasis on alignment with enterprise strategy and governance frameworks.
Requirements
- University degree in information systems, information management, computer science or a related field, or an equivalent combination of education and experience
- 3-5 years of progressive experience designing and deploying AI solutions in enterprise environments, including large language models, prompt engineering and autonomous AI workflows
- Proficiency with Azure AI, Python, SQL and Power Platform, with experience in data transformation and machine learning model development
- Experience with AI governance, responsible AI principles, data privacy, security and policy frameworks in enterprise settings
- Communication, stakeholder engagement and problem-solving skills, with the ability to translate business needs into practical AI solutions
- Strong proficiency in Cloud infrastructure
- Understanding of responsible AI principles, including bias mitigation, data privacy, and model transparency
- Experience developing and operationalizing data governance, security, and AI policy frameworks in enterprise environments
- Experience in change management and applying responsible AI principles to support ethical, compliant, and risk-informed decision-making
Nice To Haves
- Experience with ML frameworks, Copilot Studio, agentic AI, cloud infrastructure and change management is an asset
Responsibilities
- Deliver AI solutions leveraging Microsoft Azure AI services, Power Platform AI Builder, and Copilot integrations into production systems
- Implement Machine Learning (ML) and natural language processing (NLP) solutions for tasks such as classification, summarization, and predictive analytics
- Drive end-to-end model lifecycle execution, from deployment to continuous optimization, balancing enterprise-grade delivery with agile experimentation
- Establish robust monitoring and operational practices, including performance tracking, drift detection, capacity planning, version control, and production support
- Evaluate and optimize AI tools and platforms to drive continuous improvement, enhance efficiency, and reduce manual effort
- Create documentation and conduct training sessions with stakeholders to increase AI adoption
- Advance enterprise AI maturity by identifying and prioritizing high-value AI use cases aligned with business objectives, focusing on measurable impact and ROI
- Develop and maintain an AI roadmap that integrates emerging technologies (e.g., Gen AI, agentic AI) into business processes and services
- Create and operationalize governance frameworks covering privacy and security impact assessments, AI risk and compliance, and data governance standards (classification, retention, auditability)
- Lead end-to-end model lifecycle oversight, ensuring robust controls for model validation, monitoring, and regulatory alignment
- Monitor compliance of AI solutions with organizational AI ethics standards, regulatory requirements, and responsible AI principles
- Create and maintain enterprise architecture frameworks and reference models
- Collaborate with solution architects to ensure all solutions and enhancements align with existing technical architecture and business value streams
- Establish and operationalize governance frameworks for technology solution reviews, lifecycle oversight, and exception management
- Provide inputs to project teams on technology selection and integration
- Design and implement controls and monitoring processes to minimize security vulnerabilities and operational risks
Benefits
- competitive salaries
- pension
- comprehensive benefits