• Lead end-to-end solution delivery: Take ownership of requirements gathering, solution architecture, detailed design, and full lifecycle implementation of Data & AI solutions for our customers. • Serve as the technical lead on projects, ensuring that best practices in software development, testing, and deployment are followed. • Architect AI solutions: Design and build AI/ML applications using Microsoft Azure AI technologies, including Azure OpenAI (GPT-based services), Cognitive Services, Azure AI Vision/Language, machine learning models, and Copilot/Copilot Studio, to drive automation and intelligent insights. • Develop modern data platforms: Design and implement scalable data engineering solutions using Azure data services (such as Azure Data Factory, Azure Databricks, Azure Synapse Analytics, Azure Data Lake Storage) and Microsoft Fabric for unified analytics. This includes building and optimizing data pipelines (ETL/ELT), data warehouses/lakes and data models to support BI and AI workloads. • Leverage automation & Power Platform (as needed): When appropriate, utilize the Microsoft Power Platform (Power Apps, Power Automate, Power BI) to rapidly develop low-code applications, workflows, and dashboards that streamline business processes. • Collaborate across teams: Work closely with data engineers, data scientists, software developers, and business stakeholders to translate business requirements into viable technical solutions. • Client engagement & communication: Communicate complex technical concepts and articulate the business value of data and AI solutions in a clear, compelling manner to diverse audiences, from technical teams to C-level executives. • Act as a trusted advisor to clients throughout engagements, aligning solutions with their strategic objectives and ensuring client satisfaction. • Innovation and IP development: Identify new opportunities for leveraging data, AI, and automation to solve customer problems or improve our offerings. Contribute to the creation of reusable solution assets, reference architectures, and internal best-practice playbooks for Data & AI delivery, helping to continuously improve our practice. • Continuous learning: Stay current with the latest advancements in AI, machine learning, data analytics, and cloud technologies – from generative AI techniques and LLMs to updates in Azure AI Foundry and Microsoft Fabric – and assess their applicability to client use cases. Share insights with the team and drive adoption of valuable new tools or methodologies. • Pre-sales support: Support the sales and business development team by participating in early-stage client conversations, discovery workshops, and 'art of the possible' demonstrations. Review and refine technical proposals, architecture envisioning, effort estimates, and statements of work (SOWs) that accurately address client requirements.