• Act as a Strategic AI Client Director within the assigned West Coast territory, driving adoption of DDN’s AI infrastructure solutions across enterprise and research organizations. • Drive revenue growth and achieve or exceed assigned sales quota across the West Coast region. • Develop and execute strategic territory and account plans targeting organizations building large-scale AI and high-performance computing environments. • Identify and qualify new opportunities across industries including financial services, healthcare, telecommunications, technology, research institutions, and large enterprise organizations. • Build trusted relationships with senior technical leaders, AI infrastructure teams, and executive decision-makers within target accounts. • Partner with solutions architects and engineering teams to design and present AI infrastructure and high-performance data storage solutions. • Deliver compelling presentations, product demonstrations, and proposals showcasing the value of DDN’s AI-optimized storage platforms. • Manage the full enterprise sales cycle including pipeline development, forecasting, contract negotiation, and deal closure. • Develop strong relationships with channel partners, system integrators, OEM partners, and distributors to expand market reach. • Stay current on market trends related to AI infrastructure, GPU computing, large-scale model training, and high-performance data environments. • Represent DDN at industry events, conferences, and customer engagements to generate new opportunities and strengthen market presence.

Role Description Data annotation for egocentric data. - You'll do this by: Qualifications - Core Skills: - Desired Skills: Requirements - How we’d like you to lead: Benefits - Advantage Zensar Company Description We are a technology consulting and services company with 11,800+ associates in 33 global locations. More than 130 leading enterprises depend on our expertise to be more disruptive, agile and competitive. We focus on conceptualizing, designing, engineering, marketing, and managing digital products and experiences for high-growth companies looking to disrupt through innovation and velocity. - Zensar Technologies is an Equal Employment Opportunity (EEO) and Affirmative Action Employer, encouraging diversity in the workplace. - Please be assured that we will consider all qualified applicants fairly, regardless of race, creed, color, ancestry, religion, sex, national origin, citizen status, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veterans’ status. - Zensar is a place where you are free to express yourself in an environment that values individuality, nurtures development and is mindful of wellbeing. - We put our people and customers at the center of everything that we do. - Our core values include: - Putting people first - Client-centricity - Collaboration - Grow. Own. Achieve. Learn. with Zensar

• Diseño e implementación de prompts para modelos de lenguaje (LLMs), incluyendo técnicas de prompt chaining, evaluación y mejora iterativa. • Colaboración interdisciplinaria con ingenieros y stakeholders para garantizar outputs de IA interpretables y alineados al negocio. • Integración de IA en arquitecturas de software, aplicando principios de diseño y buenas prácticas de ingeniería.

• Design end-to-end AI solutions on Dataiku's platform, leveraging Dataiku Agent Hub, Prompt Studio, LLM Mesh, and Knowledge Banks (Vector Stores), or Python-based frameworks where needed. • Build and orchestrate multi-agent systems using Dataiku's Visual Agents (simple and structured), as well as code-based frameworks (LangGraph, CrewAI, Claude Agent SDK, OpenAI Agents SDK) as appropriate. • Integrate and optimize LLM APIs across providers (OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure, open-source models via Dataiku's LLM Mesh), applying model routing strategies to balance cost, latency, and quality. • Implement Retrieval-Augmented Generation (RAG) pipelines, including agentic RAG and GraphRAG, using Dataiku's Knowledge Banks with reranking, dynamic filtering, and document extraction capabilities. • Work primarily with the “Revenue” organisation, Sales, BDR, Customer Success, Solutions Engineering, Professional Services, Sales Operations and Marketing (approximately 75% of the role), and apply proven solutions and approaches more broadly across the organisation (approximately 25%). • Engage stakeholders to gather business requirements, then go further: identify the underlying user pain those requirements represent, and design solutions that address both the stated need and the deeper problem. • Own projects end-to-end, from requirements intake and solution design through to build, deployment, and handover. • Develop autonomous and semi-autonomous AI agents, using Dataiku's Agent Builder, custom Python-based architectures (LangGraph, CrewAI, Claude Agent SDK, etc.), or a combination of both. Exercise judgment on when to leverage platform capabilities and when to build custom solutions. • Design and build Agent Tools beyond documented examples, including custom API integrations, data retrieval modules, decisioning logic, and automated workflows, pushing past out-of-the-box patterns to deliver solutions tailored to specific business problems. • Build, publish, and consume MCP (Model Context Protocol) servers to enable agent-to-tool integration across systems, including designing custom MCP servers where needed. • Develop evaluation and monitoring approaches for agent systems, combining Dataiku's built-in capabilities with custom instrumentation to measure reliability, accuracy, cost, and business impact in production. • Design and maintain evaluation frameworks (evals) for LLM-based systems, measuring accuracy, latency, cost, and reliability in production. • Adhere to data governance, security, and regulatory compliance requirements (EU AI Act awareness, responsible AI practices) for all AI solutions. • Leverage Dataiku's Cost Guard and Quality Guard features to manage LLM spend, enforce usage policies, and maintain output quality standards. • Work closely with analytics and data engineering teams to maintain metadata on reference datasets for LLM consumption. • Create front-end user interfaces for AI applications using HTML, CSS, and JavaScript, within Dataiku's webapps framework, Dataiku Answers for chat-based interfaces, or standalone applications built with Vue.js and Node.js. • Collaborate on UX design, ensuring internal stakeholders find AI solutions intuitive and responsive. • Provide product feedback to the development team to improve the platform. • Stay current with the rapidly evolving AI engineering landscape, agent frameworks, model capabilities, evaluation practices, governance requirements, and tools like MCP and A2A protocols.