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Design and execute client data strategies, governance frameworks, architectures, and integrations. Lead virtual teams of data scientists, analysts, and engineers; evaluate emerging technologies; promote data literacy; and align data initiatives with business objectives. Develop KPI monitoring and reporting, support sales with data-related value propositions, present capabilities to clients, and build modern global data platforms using cloud, big data, and analytics technologies.

Lead end-to-end product design: create UI/prototypes, build and maintain design systems, run user research and testing, apply AI tools, collaborate cross-functionally, mentor designers, and ensure design-to-development handoffs.

Develops generative AI workflows and agentic systems that analyze legacy code, extract business logic, and transform it into modern implementations. Responsibilities include building language-specific parsers, orchestration layers, verification frameworks, feedback loops, ML integrations, RESTful APIs, and cloud-native infrastructure. The role collaborates with globally distributed teams to deliver production-ready modernization solutions.

Lead enterprise DevOps architecture and implementation for client solutions, focusing on Azure cloud infrastructure, automation, CI/CD, data workflows, reliability, security, and compliance. The role includes troubleshooting complex incidents, establishing performance metrics, guiding engineers, adopting improved technologies, advising executive leadership, and representing the organization externally. Candidates should bring extensive Azure, infrastructure-as-code, scripting, monitoring, data pipeline, and technical leadership experience.

Lead data strategy, architecture, governance, quality, compliance, and technology adoption. Translate stakeholder needs into scalable data initiatives, support advanced analytics and AI/ML, promote data literacy, and guide cross-functional teams toward data-driven decision-making.

Define product vision and strategy via market research and data analysis; build prioritized roadmaps; collaborate with PMs, designers, and engineers to launch products; communicate with stakeholders; track performance and iterate post-launch.

As a Principal AI/ML Engineer, you will lead AI initiatives, architect ML systems, build PoCs, collaborate with data engineers, and mentor team members, focusing on scalable solutions across various domains.

Design and implement scalable, secure solutions; translate business requirements into technical architectures; mentor development teams; create documentation and integration guides; improve systems for performance, reliability, and security; introduce new technologies and enforce architectural best practices.