Senior AI Engineer

Roles & Responsibilities 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 systemscovering 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 AI-driven 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. Other duties as required. Required Skills and Experience Bachelors or masters 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 proficiency in Microsoft AI Technologies 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. Tell employers what skills you have TensorFlowMachine LearningManagement SkillsScalabilityAzurePipelinesArtificial IntelligenceAzure Machine LearningApplication DevelopmentInformation TechnologyReliabilityData EngineeringPyTorchSQLPythonData SciencePower BIVisual Studio