Role Description The GenAI Engineer is a core technical contributor responsible for designing, building, deploying, and managing AI and Machine Learning solutions across enterprise environments. This role focuses on implementing both classical ML and modern Generative AI workloads, including agent-based systems, Retrieval-Augmented Generation (RAG), and LLM-driven pipelines. The engineer ensures all AI solutions are scalable, secure, governed, and aligned with enterprise architecture and operational requirements. Key Responsibilities - Design, build, and deliver end-to-end AI/ML solutions—from experimentation and prototyping to production deployment. - Develop AI solutions using Azure AI Foundry, Azure OpenAI, Azure Machine Learning, and related Azure AI services. - Build agent-based architectures using frameworks such as LangChain, LangGraph, Semantic Kernel, and MCP-style orchestration patterns. - Design and optimize prompt engineering strategies, RAG pipelines, embeddings, vector search, and knowledge-grounding workflows. - Build, train, evaluate, and deploy classical ML and GenAI models using Azure Machine Learning, including pipelines, feature engineering, model registry, and experiment tracking. - Implement MLOps and LLMOps practices including CI/CD, automated testing, responsible deployment, model monitoring, drift detection, and performance optimization. - Integrate AI solutions securely with enterprise systems, APIs, and event-driven architectures. - Embed Responsible AI principles—fairness, explainability, transparency, and human-in-the-loop controls—into solution design and development. - Collaborate closely with Data Engineers, AI Architects, Security teams, and business stakeholders to deliver scalable, compliant AI solutions. - Provide engineering guidance, mentor junior team members, and contribute to reusable components, shared libraries, and engineering best practices. Qualifications - Strong hands-on experience building and deploying AI solutions on Azure, including Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Azure AI Search, and Cognitive Services. - Solid understanding of machine learning concepts including feature engineering, model training, evaluation, hyperparameter tuning, and operational deployment. - Experience deploying both predictive ML and GenAI solutions in enterprise settings. - Hands-on experience with LLM-based system development, agent orchestration, and tool automation using frameworks such as LangChain, LangGraph, Semantic Kernel, and MCP-style agent communication patterns. - Experience implementing RAG pipelines, embeddings, vector databases, and document ingestion architectures. - Strong understanding of LLM constraints, prompt optimization, hallucination mitigation, and output-validation strategies. - Experience implementing CI/CD for ML and LLM workloads, including testing, monitoring, versioning, and automated deployment. - Familiarity with Azure DevOps pipelines, Git-based workflows, and cloud-native deployment automation. - Understanding of cloud-native patterns, containerization, and scalable AI infrastructure. - Knowledge of identity, access management, secrets management, and secure deployment practices for AI systems. - Familiarity with Responsible AI frameworks and enterprise governance models. - Ability to translate business problems into practical, scalable AI solutions. - Strong communication and cross-functional collaboration skills. - Experience working within Agile environments (Scrum, Kanban) delivering iteratively and incrementally. Preferred Certifications & Training - Databricks Certified Generative AI Engineer Associate - Microsoft Azure AI Engineer Associate - Azure Machine Learning Certification - Azure Data Scientist Associate (optional) - MLOps or LLMOps training - LangChain/GenAI specialization coursework Role Impact This role is central to building and scaling enterprise-ready AI capabilities. It enables the development of secure, governed, high-performing AI systems that support organizational innovation, automation, and decision intelligence. Why This Opportunity Is Attractive - Work with cutting-edge AI technologies and modern GenAI frameworks. - Lead hands-on development of AI systems deployed at enterprise scale. - Collaborate with cross-functional experts across architecture, engineering, and security. Why NTT Data? Empowerment and rewards are the cornerstone of our career development model. We are a young, fast-growing company, with a highly innovative and entrepreneurial spirit, because of this professional experience and growth will be unmatched. Our talent and positive attitude allow us to transform our goals into achievements, and projects into realities.

• Design, build, and operate core AI platform components used to train, deploy, and serve machine learning models in production environments. • Own model serving and inference workflows end-to-end, driving improvements in reliability, scalability, performance, and operational excellence. • Lead efforts to optimize inference systems for throughput, latency, and cost efficiency across CPU and GPU workloads. • Design and manage GPU-based inference and training workloads, including performance tuning, capacity planning, and resource utilization optimization. • Own and improve critical parts of the model lifecycle, including packaging, versioning, testing strategies, validation, and deployment automation. • Implement and evolve observability practices (metrics, logging, tracing, alerting) to improve visibility and operational resilience of ML services and pipelines. • Partner closely with product, infrastructure, security, and data teams to design scalable platform capabilities that enable AI-powered features. • Contribute to technical design discussions, propose architectural improvements, and mentor junior engineers through code reviews and knowledge sharing. • Participate in and help improve operational processes, including incident response, on-call rotations, and post-incident reviews.

• Design, build, and operate core AI platform components used to train, deploy, and serve machine learning models in production environments. • Own model serving and inference workflows end-to-end, driving improvements in reliability, scalability, performance, and operational excellence. • Lead efforts to optimize inference systems for throughput, latency, and cost efficiency across CPU and GPU workloads. • Design and manage GPU-based inference and training workloads, including performance tuning, capacity planning, and resource utilization optimization. • Own and improve critical parts of the model lifecycle, including packaging, versioning, testing strategies, validation, and deployment automation. • Implement and evolve observability practices (metrics, logging, tracing, alerting) to improve visibility and operational resilience of ML services and pipelines. • Partner closely with product, infrastructure, security, and data teams to design scalable platform capabilities that enable AI-powered features. • Contribute to technical design discussions, propose architectural improvements, and mentor junior engineers through code reviews and knowledge sharing. • Participate in and help improve operational processes, including incident response, on-call rotations, and post-incident reviews.

Role Description Figma is evolving the Product Support experience, powered by AI, automation, and integrated systems. The AI Infrastructure & Tooling team helps make that possible by building intelligent, resilient, and integrated solutions that automate workflows, connect systems, and streamline support operations. As a Support AI Engineer on this team, you'll be the technical execution layer that brings our support tools, customer and account context, internal systems, and AI workflows together. This role is ideal for someone who can move from ambiguous support problems to working technical solutions: - Understanding the workflow - Identifying the systems involved - Building the integration or automation - Validating the data flow - Measuring the impact on customer outcomes and Specialist efficiency This is a full-time role that can be held from one of our US hubs or remotely in the United States. What you'll do at Figma: - Build and operationalize AI-powered workflows that improve Product Support experiences for customers and internal support teams. - Design and maintain integrations across Decagon, Zendesk, Figma admin tooling, internal data sources, and adjacent Product Support platforms. - Bring relevant customer, account, product, billing, file, or admin metadata into support conversations so chatbots and Specialists have the context they need to resolve issues more effectively. - Use LLMs and AI patterns for classification, summarization, routing, recommendations, context enrichment, and workflow automation. - Partner with Engineering, Analytics, Security, Programs, Support, and vendor teams to align on requirements, implementation, governance, and rollout. - Build quality checks, monitoring, fallback paths, and operational guardrails so AI-powered workflows can be trusted in production. - Define success metrics for each workflow, track adoption and impact, and iterate based on customer outcomes, Specialist efficiency, and adoption. Qualifications - 3+ years of experience shipping integrations, automations, or internal tools across customer-facing operational systems. - Strong coding or scripting ability, including experience with APIs, webhooks, data flows, and system and workflow data integrations. - Hands-on experience with LLM-powered workflows, AI automations, or AI-enabled customer/support experiences, including working with operational data to debug issues, improve workflows, and measure impact. - Strong product and stakeholder instincts: you can translate ambiguous support problems into practical, adopted, and measurable technical solutions. - Proven track record of designing AI workflows with clear guardrails, fallback paths, and responsible deployment practices. Requirements - Experience with support platforms like Zendesk, Decagon, Sprinklr, Gainsight, Maestro QA/Rippit, Assembled, Salesforce, or similar systems. - Familiarity with agent assist tooling, AI support chatbots, copilot tooling, RAG, AI observability, or monitoring AI workflows in production. - Experience building internal Slack tooling, workflow automations, or embedded support experiences. - Background in Support Engineering, Internal Tools Engineering, Solutions Engineering, Support Operations, CX Systems, or Business Systems. - Familiarity with customer support metrics such as containment, deflection, CSAT, first contact resolution, routing accuracy. Benefits - Equity to employees - Competitive package of additional benefits, including health, dental & vision - Retirement with company contribution - Parental leave & reproductive or family planning support - Mental health & wellness benefits - Generous PTO - Company recharge days - Learning & development stipend - Work from home stipend - Cell phone reimbursement - Sales incentive pay for most sales roles - Annual bonus plan for eligible non-sales roles