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 Google Cloud Platform), 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, governance, and compliance reporting.
Consulting and Communication
Strong consulting, stakeholder management, and customer engagement skills - able to build trust with technical teams, business operations, and senior leadership.
Ability to communicate effectively with both domain/business stakeholders and technical teams, adapting the level of detail to the audience.
Ability to work independently, manage ambiguity, and balance business, technical, and operational priorities within customer environments.
Strong product feedback mindset - ability to observe field realities and translate them into actionable, structured product improvement recommendations.
Collaboration and knowledge-sharing orientation - contributing to team capability through documentation, playbooks, and mentoring.
Required Qualifications and Certifications
Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline, or equivalent combination of education and experience.
Relevant certifications in cloud technologies (AWS Solutions Architect, Azure Developer/Administrator, Google Cloud Platform Professional Cloud Architect), Kubernetes (CKA/CKAD), or AI/ML are highly beneficial.
Domain certifications in insurance (CPCU, AINS, or equivalent) or manufacturing (Six Sigma, APICS, or equivalent) are a plus.
Required Experience
Typically 6-8 years of experience in software engineering, solution architecture, DevOps, platform engineering, customer engineering, or related technology disciplines.
Experience working directly with enterprise customers in customer-facing, consultative environments.
Experience deploying and operating containerized applications in production - Kubernetes, Helm, CI/CD pipelines, container registries, and infrastructure automation.
Experience with Python in a production context - building APIs, scripting automation, integrating with cloud services, or developing data processing pipelines.
Experience with at least one major cloud platform (AWS, Azure, or Google Cloud Platform) in a hands-on engineering capacity.
Experience implementing, configuring, or extending enterprise platforms or products within customer environments.
Experience delivering technology solutions in one or more of the following domains: insurance (underwriting, claims, policy administration, TPA operations), manufacturing (quality, supply chain, compliance, production operations), or adjacent regulated industries.
Experience translating domain-specific workflows, business rules, and operational constraints into technology solution configurations.
Experience working with AI/ML systems in production - model integration, confidence-based routing, human-in-the-loop workflows, or document processing pipelines.
Experience providing structured field feedback to product or engineering teams, distinguishing between customer-specific needs and reusable platform improvements.
Experience operating independently and solving complex business and technical challenges within ambiguous or evolving environments."
About NTT DATA
NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.
Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client's needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com, @nttdatafed.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form, ;br>
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Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 80166078
- Position Id: 7277f926371808ee8b3ddf8e14cbd0ae
- Posted 30+ days ago
