• Design, implement, and ship AI systems that automate work, support decisions, and unlock new workflows for enterprise clients • Building multi-step agents and tool-using LLM systems • Wiring AI systems into structured and unstructured data sources • Packaging AI systems into services for consumption by other engineers and applications • Own the system evaluations, observability, and the feedback loops for iteration • Collaborate closely with strategists, engineers, and designers throughout the project lifecycle

• Design and implementation of production-ready AI and agent systems • Development of modern architectures around LLMs, knowledge graphs, and data integration • Operation and scaling of AI workloads on Kubernetes • Responsibility for deploying and optimizing on-prem LLMs (e.g., vLLM, Ollama) • Leading architecture workshops and advising clients up to C-level • Making informed technology decisions and evaluating new frameworks • Active contribution to shaping the AI portfolio

• Own end-to-end delivery of features and projects: design, implementation, deployment, and the client conversation around them • Pair with senior engineers on complex builds, owning meaningful slices independently • Build production AI workflows: RAG components, integrations across cloud platforms, agentic automations • Review pull requests from associate engineers • Write design notes that the team uses; contribute to architectural conversations

• Define and execute a company-wide AI strategy that ensures alignment between business objectives and technological innovation. • Spearhead transformational projects and deliver quantifiable value through AI across the organization. • Identify and prioritize high-impact AI use cases to reduce manual work and improve quality. • Ensure that AI initiatives translate into tangible outcomes such as productivity gains and cost efficiencies. • Create reusable AI tools to enable scalable adoption. • Lead an AI governance forum to prioritize use cases and manage resources. • Operationalize AI and integrate it into workflows. • Collaborate with HR and leadership for fostering AI literacy across the organization. • Measure metrics such as adoption and productivity uplift. • Ensure secure and responsible AI adoption by working closely with legal and data protection teams.