Forward-Deployed Engineer (FDE)

NTT DATA Services•Plano, TX

About The Position

The Forward-Deployed Engineer deploys, configures, extends, and operationalizes NTT DATA’s industry-specific AI platforms for Insurance and Manufacturing within customer environments. Powered by the AI Vista Platform, these platforms use pre-built agents, domain ontologies, governed workflows, and composable building blocks to automate back-office operations through AI-driven workflows, with human oversight where appropriate. The engineer configures extraction schemas and business rules, registers connectors, assembles workflows, deploys containerized services, and supports governed, observable production operation. The role combines agentic AI engineering, domain understanding, DevOps capability, and customer engagement. The engineer manages implementations from discovery through production and adoption, and channels field insights, reusable patterns, and product improvement opportunities to platform product teams. The position is based in Plano, Texas; work arrangements may vary according to client requirements and business needs.

Requirements

Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related discipline, or an equivalent combination of education and 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, including Kubernetes, Helm, CI/CD pipelines, container registries, and infrastructure automation.
Production-context Python experience, such as building APIs, scripting automation, integrating cloud services, or developing data-processing pipelines.
Hands-on engineering experience with at least one major cloud platform: AWS, Azure, or GCP.
Experience implementing, configuring, or extending enterprise platforms or products within customer environments.
Experience delivering technology solutions in insurance, manufacturing, or adjacent regulated industries.
Experience translating domain-specific workflows, business rules, and operational constraints into technology solution configurations.
Experience with AI/ML systems in production, such as model integration, confidence-based routing, human-in-the-loop workflows, or document-processing pipelines.
Experience providing structured field feedback to product or engineering teams and distinguishing customer-specific needs from reusable improvements.
Ability to operate independently and solve complex business and technical challenges in ambiguous or evolving environments.
Strong understanding of agentic AI architectures, including multi-agent systems, tool use (MCP), RAG, and workflow orchestration.
Working knowledge of at least one major hyperscaler and associated managed AI/ML services, container services, managed databases, blob storage, and identity providers.
Familiarity with LLM concepts, including prompt engineering, model selection and routing, confidence scoring, token economics, and deterministic logic versus neural reasoning.
Understanding of document-processing pipelines, including 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, including OpenTelemetry traces, metrics, and logs, structured logging, and production monitoring dashboards.
Understanding of infrastructure-as-code principles, such as Terraform or equivalent, and GitOps workflows.
Ability to quickly learn and apply 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, including audit trails, data lineage, governance, and compliance reporting.
Strong consulting, stakeholder management, and customer engagement skills, including the ability to build trust with technical teams, business operations, and senior leadership.
Ability to communicate effectively with business and technical audiences, adapting the level of detail to the audience.
Ability to manage ambiguity and balance business, technical, and operational priorities within customer environments.
Strong product feedback mindset and ability to turn field observations into structured, actionable product improvement recommendations.
Collaboration and knowledge-sharing orientation, including contributing documentation and playbooks and mentoring others.

Nice To Haves

Cloud certifications such as AWS Solutions Architect, Azure Developer/Administrator, or GCP Professional Cloud Architect.
Kubernetes certifications such as CKA or CKAD.
AI/ML certifications.
Insurance domain certifications such as CPCU or AINS, or equivalent.
Manufacturing domain certifications such as Six Sigma or APICS, or equivalent.

Responsibilities

Partner with customer stakeholders to understand business objectives, operational pain points, and desired outcomes.
Lead discovery and solution planning, translating customer business processes into deployable AI-powered workflows.
Manage the end-to-end implementation lifecycle, including discovery, design, configuration, deployment, adoption, and optimization.
Configure customer-specific extraction schemas, business rules, confidence thresholds, connectors, and multi-step agentic workflows.
Build custom agents and integrations when requirements extend beyond pre-built platform capabilities.
Translate standard operating procedures and business rules into executable platform configurations in collaboration with domain subject matter experts.
Deploy platform services to customer environments using Helm charts on Kubernetes, and manage 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, including OpenTelemetry, dashboards, and alerting, for production operation.
Develop and maintain working expertise in AI for Insurance, AI for Manufacturing, or both, and apply relevant domain context to platform configuration and solution design.
Serve as the primary feedback channel between customer implementations and platform product teams.
Distinguish customer-specific customization needs from reusable platform improvements.
Contribute reusable implementation assets, including deployment playbooks, configuration templates, and integration patterns.