Principal Architect - Agentic AI & AI-Native Software Engineering

Company Overview

Req ID: 395474

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 professional to join our team in Mahwah, New Jersey (US-NJ), United States (US).

• AI-Native Engineering Operating Model:
  • Design the target operating model for small, outcome-oriented engineering pods that combine human product/domain expertise with specialized AI agents.
  • Define the optimal division of work between product/domain leaders, full-stack engineers, and AI agents across requirements, architecture, development, testing, security, release, operations, and documentation.
  • Create clear human-in-the-loop controls, approval points, escalation paths, and accountability for agent-generated work.
  • Partner with delivery leaders to transition fragmented staffing models into cohesive, accountable pods with end-to-end ownership of outcomes.
  • Define skill profiles and upskilling pathways that enable engineers to operate as AI-enabled full-stack product builders rather than narrow functional specialists.
• Agentic SDLC Architecture & Engineering:
  • Architect end-to-end agentic workflows spanning discovery and requirements, solution design, code generation, code review, test generation and execution, security checks, CI/CD, deployment, observability, incident analysis, and technical documentation.
  • Design and deploy autonomous and semi-autonomous agents with planning, reasoning, memory, context management, tool/function calling, and multi-agent collaboration capabilities.
  • Implement agent orchestration patterns that allow multiple specialized agents to collaborate reliably while maintaining deterministic controls for high-risk actions.
  • Integrate agents with engineering platforms and enterprise systems such as GitHub, Azure DevOps, JFrog, cloud services, observability platforms, service management tools, databases, APIs, and internal knowledge sources.
  • Ensure agentic workflows are designed for production use, including resilience, fallback behavior, auditability, traceability, and secure access to enterprise systems.
• Reusable Agentic Engineering Blueprints:
  • Identify successful AI-enabled and agentic engineering patterns already demonstrated within delivery teams and convert them into reusable reference architectures, playbooks, templates, and accelerators.
  • Create standardized blueprints for common engineering use cases including requirements decomposition, story creation, coding, self-review, test automation, defect analysis, security analysis, log analysis, release automation, and operational support.
  • Design the enablement approach required to replicate proven patterns across multiple products, portfolios, and engineering pods without creating bespoke implementations for every team.
  • Establish a pattern library and reusable agent catalog with clear ownership, versioning, guardrails, and adoption guidance.
• Engineering Productivity & Business Outcome Measurement:
  • Establish productivity baselines before agentic transformation and define measurable target improvements for each pod.
  • Quantify changes in throughput, cycle time, lead time, time-to-production, release frequency, quality, escaped defects, engineering effort, cost, and roadmap delivery.
  • Design controlled experiments that compare traditional, AI-assisted, and agentic delivery models and isolate the impact of AI-enabled ways of working.
  • Translate engineering productivity improvements into executive-level business outcomes, such as accelerated roadmap milestones, increased feature throughput, reduced operating cost, improved service quality, and faster realization of business value.
  • Build executive dashboards that show pod-level and portfolio-level AI productivity, ROI, adoption, quality, and outcome metrics.
• Context Engineering, Knowledge & Legacy Modernization:
  • Design enterprise context architectures that give agents reliable access to product knowledge, business rules, architecture standards, source code, technical documentation, and operational data.
  • Architect RAG, knowledge graph, semantic search, and persistent context-store patterns that improve agent accuracy and reduce repeated context loading.
  • Apply context engineering to modernization and reimagination efforts while preserving the role of product owners and domain SMEs in business-process redesign and decision making.
  • Ensure modernization solutions distinguish between technology acceleration and true business-process reimagination; use AI to augment domain judgment rather than assume it can be eliminated.
  • Define methods for maintaining context freshness, provenance, access controls, and traceability.
• AI Economics, Token Optimization & Model Strategy:
  • Establish token consumption and cost baselines by agent, model, workflow, product, and pod.
  • Design intelligent model-routing strategies that select the right model based on task complexity, quality requirements, latency, security, and cost.
  • Optimize context windows, prompt strategies, caching, retrieval, persistent context, and model usage to reduce unnecessary token consumption.
  • Define cost-per-outcome and cost-per-engineering-work-unit measures for agentic delivery.
  • Continuously evaluate new models and agent platforms based on measurable quality, reliability, security, and economics - not novelty alone.
• Security, Governance & Responsible AI:
  • Define enterprise guardrails for agent identity, authorization, secrets, tool access, data handling, auditability, model risk, and secure execution.
  • Implement controls for prompt injection, data leakage, unsafe tool execution, hallucinations, model misuse, and inappropriate autonomous action.
  • Design observability for agent decisions, tool calls, failure modes, latency, cost, and quality.
  • Partner with security, risk, compliance, architecture, and legal stakeholders to ensure solutions meet enterprise Responsible AI and governance requirements.
  • Create release and operational-readiness criteria for production agents.
• Technical Leadership & Transformation:
  • Act as the senior technical leader for agentic engineering transformation across client and delivery organizations.
  • Partner with product, business, architecture, and engineering leaders to prioritize use cases based on measurable business value and readiness for agentic delivery.
  • Coach architects, engineers, delivery leaders, and product teams on AI-native engineering practices and human-agent collaboration.
  • Lead architecture reviews, design sessions, pilots, and production-readiness assessments.
  • Communicate architecture, operating-model changes, value realization, risks, and recommendations clearly to senior executives and client stakeholders.

Required Qualifications

• Experience:
  • 12-15 years of overall technology experience, including 5 years in architecture or principal engineering roles.
  • Demonstrated hands-on experience designing and deploying production-grade Generative AI and Agentic AI solutions.
  • Proven experience redesigning software-engineering workflows or delivery operating models using AI and automation.
  • Experience architecting multi-agent systems, agent orchestration, tool-calling workflows, enterprise context, and human-in-the-loop controls.
  • Experience measuring engineering productivity and translating technical improvements into business outcomes.
  • Experience operating in large, complex enterprises with strict security, compliance, integration, and change-management requirements.
  • Strong consulting, stakeholder-management, and executive communication skills.
• Education:
  • Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related field.
  • Master's degree or equivalent advanced technical experience preferred.

Required Technical Capabilities

• Agentic AI: Multi-agent architecture; agent orchestration; planning and reasoning; memory and context; tool/function calling; human-in-the-loop design; evaluation; guardrails.
• AI-Native SDLC: AI-assisted and agentic requirements, design, coding, review, testing, security, CI/CD, release, observability, operations, and documentation.
• GenAI & Context Engineering: LLMs, prompt engineering, RAG, embeddings, vector/semantic search, knowledge graphs, context stores, model adaptation, and evaluation.
• Primary Ecosystem: Azure OpenAI, Azure AI Foundry, GitHub Copilot / GitHub platform, Microsoft Copilot Studio, Semantic Kernel, and enterprise Azure services.
• Agent Frameworks: Strong experience with one or more of LangGraph, OpenAI Agents SDK/Framework, AutoGen, Semantic Kernel, CrewAI, or equivalent orchestration frameworks.
• Software Engineering: Python required; strong understanding of APIs, event-driven architecture, microservices, full-stack engineering, secure coding, and software architecture.
• DevSecOps / Platform: GitHub Actions and/or Azure DevOps, CI/CD, containers, Kubernetes, artifact repositories, secrets, policy-as-code, monitoring, and observability.
• Enterprise Integration: REST/GraphQL APIs, eventing, databases, enterprise identity, service management, internal developer platforms, and major business applications as required.
• Measurement & AI FinOps: Engineering productivity measurement, experimentation, AI ROI, token economics, model routing, latency/quality/cost tradeoffs, and usage observability.

Preferred Experience

• Experience transforming traditional 8-10 person software delivery teams into smaller AI-augmented pods.
• Experience with product-centric operating models and the convergence of traditional BA, QA, developer, and operations responsibilities into AI-enabled product teams.
• Experience modernizing legacy applications using context engineering, knowledge graphs, or software digital twins.
• Experience in transportation, logistics, supply chain, industrial operations, or other large-scale mission-critical environments.
• Relevant Microsoft Azure, GitHub, Generative AI, or Agentic AI certifications.

About NTT DATA

NTT DATA is a $30 billion trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize, and transform for long-term success. As a Global Top Employer, we have diverse experts in more than 50 countries and a robust partner ecosystem of established and start-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation, and management of applications, infrastructure, and connectivity. We are one of the leading providers of digital and AI infrastructure in the world. NTT DATA is a part of NTT Group, which invests over $3.6 billion each year in R&D to help organizations and society move confidently and sustainably into the digital future. Visit us at us.nttdata.com

NTT DATA provides a reasonable range of compensation for U.S.-based positions. The starting pay range for this remote role is $90/Hr to $100/Hr. This range reflects the minimum and maximum target compensation for the position across all US locations. Actual compensation will depend on a number of factors, including the candidate’s actual work location, relevant experience, technical skills, and other qualifications. This position is eligible for company benefits including participation in medical, dental, and vision insurance, flexible spending or health savings account, and AD&D insurance, employee assistance, participation in a 401k program, and additional voluntary or legally-required benefits.

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