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NTT Data

Global IT services and consulting provider.

AI Architect

AI Application EngineeringUpdated on Nov 14
Full-timePhiladelphia, PA, USA

About the Job

Requirements

  • A bachelor’s degree in computer science, engineering, data science, information systems, or a related technical discipline.
  • At least 8 years in software engineering, cloud, data engineering, AI/ML, architecture, or enterprise technology delivery.
  • At least 4 years designing and delivering production AI or machine learning solutions.
  • At least 1 year delivering production Generative AI or large language model-based systems, including large language model APIs, retrieval-augmented generation, embeddings, vector and graph-based retrieval, evaluation, guardrails, and agentic workflows.
  • At least 5 years in software engineering with professional-level proficiency in at least one mainstream language—Python, Java, C#, C++, TypeScript/JavaScript, Go, or equivalent—including designing and integrating APIs and backend services using REST, gRPC, event-driven, or asynchronous patterns.
  • At least 3 years delivering technical design and implementation of significant components in complex enterprise environments.
  • At least 2 years architecting AI solutions across more than one deployment model (public cloud plus private cloud, hybrid, or on-premises) and establishing operational practices for AI systems—model and prompt versioning, artifact and experiment tracking, automated evaluation, monitoring and tracing, incident response, and rollback—using CI/CD, infrastructure as code, and DevOps tooling.

Responsibilities

  • Lead assigned AI projects from conception through deployment, owning design and delivery for accountable workstreams.
  • Translate business challenges into AI solution designs and evaluate the business impact and expected return of proposed approaches.
  • Design and implement major components of enterprise AI solutions across applications, integration, data, AI/ML, and infrastructure.
  • Design and build AI solution patterns across machine learning and Generative AI, including large language model applications, retrieval-augmented generation, agents, orchestration, embeddings, vector and graph-based retrieval, model integrations, and evaluation.
  • Conduct data preprocessing, feature engineering, and dataset preparation for AI workloads.
  • Build prototypes, proofs of concept, reference implementations, and production-ready AI services.
  • Optimize AI models and inference workloads for performance, scalability, and cost, including on distributed computing frameworks and AI-optimized hardware.
  • Design data storage and retrieval approaches for AI workloads across databases, data lakes, and vector or graph stores.
  • Apply secure-by-design practices, including authentication, role-based access control, secrets management, encryption, private networking, data protection, auditability, and authorization-aware data retrieval.
  • Implement responsible-AI controls, including content safety, personally identifiable information protection, bias identification and mitigation, prompt-injection defenses, and human review.
  • Work within client data governance requirements and applicable data privacy regulations in all designs and deliveries.
  • Apply MLOps, LLMOps, and GenAIOps practices, including CI/CD, version control, model and prompt versioning, infrastructure as code, testing, evaluation, monitoring, tracing, and lifecycle management.
  • Communicate AI concepts, design decisions, and trade-offs to client technical, product, and business teams, and collaborate with delivery, data, infrastructure, and security functions to integrate AI solutions.
  • Mentor engineers and junior team members in AI engineering and architecture practices.
  • Contribute reusable patterns, accelerators, and delivery playbooks; stay current with emerging AI technologies; and provide technical input to discovery workshops, solution shaping, and effort estimates.

Desired Qualifications

  • A master’s degree in AI/ML, computer science, engineering, data science, or a related field.
  • At least 2 years in consulting or professional services delivering technical solutions to external clients, including discovery workshops, solution shaping, effort estimation, and proposal input.
  • At least 2 years architecting secure enterprise systems—including identity and authentication, authorization and role-based access control, secrets management, encryption, network isolation, and audit logging—and implementing production responsible-AI controls such as content safety, personally identifiable information protection, prompt-injection defenses, human review, and model risk management.
  • Experience spanning Generative AI and one or more additional AI domains, such as classical machine learning, predictive analytics, natural language processing, computer vision, or intelligent automation.
  • Experience across two or more major cloud AI platforms, including managed model, retrieval, and orchestration services.
  • Experience with container orchestration and GPU or accelerator infrastructure.
  • Experience with model-serving and inference-optimization stacks for self-hosted and private deployments, whether open-source or vendor-provided.
  • Experience with modern deep learning and classical machine learning frameworks and the wider open-source model ecosystem.
  • Experience with knowledge graphs, ontologies, semantic modelling, and graph databases, including graph-based or hybrid retrieval approaches for AI systems.
  • Experience with model fine-tuning, quantization, evaluation, model routing, inference optimization, or deployment of open-source and proprietary models.
  • Experience in AI governance, data privacy, security architecture, responsible AI, compliance, model risk, observability, and production AI operations.
  • Relevant architecture, cloud, or AI certifications are welcome but not required.

About the Company

NTT Data

Global IT services and consulting provider.

NTT DATA is a global IT and business services provider that helps organizations run and transform their technology and operations. It delivers consulting, industry solutions, cloud migration, cybersecurity, data analytics, and managed services to modernize systems and processes. It differentiates itself through its large scale, deep sector expertise in banking, healthcare, manufacturing, public sector, and automotive, and its broad ecosystem of partners with Microsoft, SAP, AWS, and Salesforce. Its goal is to help large enterprises digitally transform and optimize operations at scale using cloud-native approaches and secure IT environments.

Company size
10,001+
Company stage
IPO
Headquarters
Tokyo, Japan
Founded
1988

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