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Design, develop, and operationalize scalable AI and machine learning solutions for enterprise clients. Build production models, data pipelines, and DevOps/MLOps workflows; customize deep learning, generative AI, and large language models; work with large datasets and distributed or cloud environments; conduct AI and HPC research; and collaborate with business and technical stakeholders to deliver reliable solutions.

Support digital product design across research, ideation, UX flows, wireframes, prototypes, and UI screens. Contribute to design systems, accessibility, and inclusive design while collaborating with researchers, engineers, content designers, strategists, clients, and senior designers. Manage assigned workstreams, present design thinking, incorporate feedback, and participate in studio and design-community initiatives.

The Technical Business Analyst will translate client needs into business requirements, process maps, business cases, and technical solutions. Responsibilities include stakeholder analysis, workshop facilitation, Agile product and project delivery, risk and dependency management, and communication with senior stakeholders. The role also involves interpreting data models, integration diagrams, and platform documentation; collaborating with data and technology specialists; and supporting AI-enabled process improvements using platforms such as Databricks, AWS, and GCP.

Design, develop, test, and deploy scalable full-stack applications on Google Cloud Platform. Build cloud-native applications, APIs, data services, microservices, and modern user experiences while supporting architecture, modernization, automation, and continuous improvement. Collaborate with clients, architects, and distributed teams to deliver secure, high-performance enterprise solutions using Agile, DevOps, CI/CD, and Google Cloud technologies.

Leads AI-enabled process transformation methodology across client engagements. Defines process redesign, benchmarking, facilitation, governance, operating model, and AI agent experience standards. Manages client workshops, senior stakeholder presentations, final design approvals, and collaboration with technology partners. Develops consulting teams, coaches senior specialists, applies industry expertise, and scales AI-enabled solutions across a portfolio of engagements.

Own the end-to-end lifecycle of software products, including strategy, roadmaps, releases, business cases, pricing, commercial frameworks, and deal structures. Conduct market research, support sales initiatives, manage stakeholder and technology partnerships, collaborate with product marketing, and evaluate enhancements based on customer and market needs. The role requires SAP Enterprise Asset Management experience and strong client relationship management, strategic planning, and program management skills.

The job description provides no role-specific responsibilities or qualifications. Based on the title, the position appears to involve managing forward-deployed engineering work, but further details were not provided.

The provided description contains no role-specific responsibilities or qualifications. Based on the title, this position appears to manage engineering teams delivering solutions directly to client environments in a forward-deployed capacity.

Design, develop, and operationalize scalable AI and machine learning solutions for enterprise clients. Build production ML models, data pipelines, MLOps and LLMOps workflows, cloud-based systems, and high-performance computing applications. Apply deep learning and generative AI to complex business problems, manage large datasets, ensure system reliability, and collaborate with business and technical stakeholders to deliver production-ready outcomes.

Designs and delivers enterprise AI architectures spanning generative AI, classical machine learning, LLMs, agentic systems, data pipelines, RAG, model platforms, security, governance, observability, and FinOps. Leads technology selection, architecture decisions, prototypes, reference architectures, and technical workshops. Owns architecture artifacts, mentors cross-functional engineering teams, and ensures scalable, secure, production-ready AI systems aligned with client business objectives.