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Advise enterprise clients on creating and capturing value from AI and data. Define AI and data strategies, assess organizational maturity, develop platform blueprints, operating models, business cases, investment profiles, and delivery roadmaps. Embed Responsible AI, shape AI-first products, identify commercialization opportunities, lead large-scale transformation workstreams, engage C-suite stakeholders, build teams, and support deal origination and go-to-market activities.

Design, build, and operationalize AI/ML solutions including deep learning, generative AI, and LLMs. Develop scalable data, DevOps and MLOps pipelines, work with large datasets and HPC/edge environments, and translate technical approaches into business value while collaborating with cross-functional and client teams.

Partner with sales and customer teams to provide pre-sales technical expertise: run technical discovery, design API integrations and solution architectures, address security/compliance concerns, deliver demos/POCs, and support onboarding and cross-functional handoffs to accelerate customer adoption.

Leads knowledge engineering and AI initiatives, directing the design and delivery of knowledge graphs, semantic solutions, ontologies, graph-based machine learning, and enterprise AI pipelines. Manages teams and workstreams, establishes technical standards, advises clients, develops business cases, supports sales and presales, and provides thought leadership on emerging AI, data, and semantic technologies.

Designs, develops, and deploys scalable AI and machine learning solutions for enterprise clients. Responsibilities include model selection and customization, deep learning and generative AI development, data and MLOps/LLMOps pipeline design, production monitoring, large-scale data processing, distributed and high-performance computing, research, and client-facing collaboration to deliver reliable business outcomes.

Design, build, and deploy production-grade agentic AI systems at enterprise scale. Lead client engineering engagements, define RAG and multi-LLM standards, own LLMOps (eval, prompt governance, observability), implement cloud-native architectures, and publish reusable patterns. Manage and develop engineering teams and measure agentic system quality across accuracy, latency, safety, and cost.

Embed with client teams to deploy, scale, and operationalize enterprise AI platforms end-to-end. Drive production outcomes (time-to-value, reliability, adoption, scalability), design enterprise AI architectures, build reusable patterns, lead workshops and prototypes, and translate technical solutions into quantifiable business impact for senior stakeholders.

The role involves advising clients on payroll transformation using Workday software, configuring payroll systems, conducting testing, and providing training.

Design, build, and deploy production-grade agentic AI systems: multi-agent orchestration, RAG pipelines, provider abstraction, LLMOps (eval harnesses, prompt versioning, observability), cloud-native deployment (Kubernetes/Docker/CI-CD/IaC), and embed with client teams to deliver reusable patterns and measurable metrics for accuracy, latency, safety, and cost.

Embed with client teams to deploy, scale, and operationalize enterprise AI platforms. Own production outcomes from prototype to production, design enterprise AI architectures across identity, data, security, governance, and build reusable playbooks and accelerators to ensure adoption, reliability, and scalability.