AI Principal Engineer

Req ID:   387296   NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now. We are currently seeking a Forward-Deployed Engineer (FDE) - Insurance to join our team in Plano, Texas (US-TX), United States (US). "Forward Deployment Engineer — AI Products Role Summary The Forward Deployed Engineer — AI Products (FDE) deploys, configures, extends, and operationalizes NTT DATA's industry-specific AI platforms — AI for Insurance and AI for Manufacturing — within customer environments. These platforms, powered by the AI Vista Platform, are designed for Service as Software (SaS) — replacing manual, labor-intensive back-office operations with AI-driven agentic workflows that deliver outcomes autonomously, with human oversight where it matters. The platforms provide pre-built specialized agents, domain ontologies, governed workflows, and composable building blocks. The FDE takes these capabilities into production for specific customers — configuring extraction schemas, authoring business rules, registering connectors, assembling workflows, deploying containerized services, and ensuring governed, observable operation. The FDE combines agentic AI engineering expertise with domain understanding, DevOps capability, and customer engagement skills. They manage implementations from discovery through production, and serve as a critical feedback channel — surfacing field insights, reusable patterns, and product improvement opportunities back to the platform product teams. ________________________________________ Key Responsibilities Customer Engagement and Solution Delivery •    Partner with customer stakeholders to understand business objectives, operational pain points, and desired outcomes. •    Lead discovery and solution planning to translate customer-specific business processes into deployable AI-powered workflows. •    Manage the end-to-end implementation lifecycle: discovery, design, configuration, deployment, adoption, and optimization. Platform Configuration and Extension •    Configure the platform for customer-specific use cases — extraction schemas, business rules, confidence thresholds, connectors, and multi-step agentic workflows. •    Build custom agents and integrations where customer requirements extend beyond pre-built platform capabilities. •    Translate customer standard operating procedures and business rules into executable platform configurations, working alongside domain subject matter experts. DevOps, Deployment, and Operations •    Deploy platform services to customer environments using Helm charts on Kubernetes, managing container registries, deployment pipelines, and environment-specific configuration. •    Automate repeatable, auditable deployment pipelines for platform updates and agent image releases. •    Configure observability and monitoring infrastructure (OpenTelemetry, dashboards, alerting) for production-grade operation. Domain Specialization •    Develop and maintain working expertise in one or both platform domains: o    AI for Insurance: SaS workflows that replace manual underwriting, claims, policy servicing, and TPA back-office operations with AI-driven extraction, classification, decisioning, and document generation. o    AI for Manufacturing: SaS workflows that replace manual quality management, supply chain documentation, compliance reporting, and production operations with AI-driven inspection, validation, and reporting. •    Apply domain context — industry processes, terminology, regulations, and KPIs — to platform configuration and solution design decisions. Feedback and Product Improvement •    Serve as the primary feedback channel between customer implementations and platform product teams. •    Distinguish customer-specific customization needs from reusable platform improvements that benefit all future deployments. •    Contribute to reusable implementation assets: deployment playbooks, configuration templates, and integration patterns. ________________________________________ Knowledge and Attributes Technical •    Strong understanding of agentic AI architectures, including multi-agent systems, tool use (MCP — Model Context Protocol), retrieval-augmented generation (RAG), and workflow orchestration patterns. •    Proficiency in Python for scripting, automation, agent development, API integration, and data transformation. •    Working knowledge of at least one major hyperscaler (AWS, Azure, or GCP), including managed AI/ML services (Bedrock, Azure AI Foundry, Vertex AI), container services, managed databases, blob storage, and identity providers. •    Experience with containerized deployments: Docker, Kubernetes, Helm charts, container registries, and CI/CD pipelines for automated deployment. •    Familiarity with LLM concepts: prompt engineering, model selection and routing, confidence scoring, token economics, and the distinction between deterministic logic and neural reasoning. •    Understanding of document processing pipelines: OCR, structured field extraction, classification, confidence-based routing, and human-in-the-loop review workflows. •    Working knowledge of PostgreSQL, REST API design, and event-driven architectures. •    Familiarity with observability tooling: OpenTelemetry (traces, metrics, logs), structured logging, and production monitoring dashboards. •    Understanding of infrastructure-as-code principles (Terraform or equivalent) and GitOps workflows. Domain and Business •    Ability to quickly learn and apply domain context — industry processes, terminology, regulations, document types, data flows, user roles, and success metrics. •    Commercial and domain awareness to connect technical configuration decisions with business value, risk reduction, operational efficiency, and compliance requirements. •    Understanding of regulated industry requirements: audit trails, data lineage, govern