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Define and lead AI/GenAI strategy for infrastructure, architect enterprise MLOps/GenAIOps on cloud, build scalable model pipelines and RAG/LLM workflows, optimize GPU/TPU provisioning, implement AI governance and security, integrate with enterprise tools, advise clients, mentor specialists, and support business development.

As a Workday Talent & Performance Consultant, you will lead implementations of Workday solutions and collaborate with clients to enhance HR processes through technology.

As a MES Implementation Engineer, you will develop, configure, and test MES solutions, integrate them with ERP and other systems, and work in an Agile environment. You will enhance automation solutions to meet customer requirements.

Develop and maintain embedded C++ software, optimize Ethernet communication stacks, manage network communication, and collaborate with teams for debugging and development.

Design, build, migrate, operate and govern hybrid cloud and on-prem infrastructure. Architect and deliver cloud transformations, implement virtualization, container platforms, automation, and resilience strategies.

Deploy and adapt existing ML models, integrate processing pipelines into Airflow and APIs, transform data from MinIO and PostgreSQL, optimize PySpark for monthly alerts, and manage ML workflow dependencies, sequencing, stages, and deployments.

Design and define end-to-end scalable, resilient AI architectures (data pipelines, model training/serving, retrieval, monitoring, governance), collaborate with delivery teams, contribute to proposals, mentor engineers, and evaluate emerging AI tools and technologies.

Lead business-process discovery and translate stakeholder requirements into ServiceNow solutions. Facilitate workshops, document and optimize processes, build user stories and acceptance criteria within Agile/SAFe, support UAT and training, and provide functional ServiceNow expertise to ensure adoption and value delivery.

Lead design, build, and deployment of production-grade agentic AI systems for enterprise clients. Architect multi-agent orchestration, RAG pipelines, multi-LLM integrations, and programme-scale LLMOps (eval, observability, safety, cost). Lead client engineering engagements, publish reusable patterns and standards, and manage an engineering team to deliver reliable, observable agentic solutions.

Lead complex, end-to-end technology sales cycles for financial services, engage C-level clients, craft value propositions across cloud, data/AI, core banking and cybersecurity, shape commercial models, orchestrate deal teams, build partner offerings, and drive pipeline and origination initiatives to close multi-million deals.