AI Architect

NTT DATA Services • Philadelphia, PA

3d • $155,700 - $259,500

About The Position

NTT DATA is hiring an AI Architect at Lead Consultant level to lead client AI projects from conception through deployment. The role translates business challenges into working AI solutions and owns the design and delivery of assigned workstreams. It spans discovery, solution design, proofs of concept, production implementation, and operational improvement across machine learning, predictive analytics, natural language processing, computer vision, intelligent automation, and Generative AI.

This is a hands-on architecture role, with approximately 70% of time spent building reference implementations, prototypes, production services, and integrations, and approximately 30% spent on design, technical leadership, and mentoring. Engagement models vary, and some client engagements require sustained hands-on delivery alongside the engineering team.

The position is based in Philadelphia, Pennsylvania. Work arrangements may be remote or hybrid where available, but are subject to change based on client requirements; in-office attendance may be required for employees near an NTT DATA office or client site.

Requirements

• 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 ML solutions.
• At least 1 year delivering production Generative AI or LLM-based systems, including LLM APIs, RAG, embeddings, vector and graph-based retrieval, evaluation, guardrails, and agentic workflows.
• At least 5 years of software engineering experience and professional-level proficiency in at least one mainstream language such as Python, Java, C#, C++, TypeScript/JavaScript, or Go; experience 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, including 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.

Nice To Haves

• 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 and implementing production responsible-AI controls, including identity and authentication, authorization and RBAC, secrets management, encryption, network isolation, audit logging, content safety, PII protection, prompt-injection defenses, human review, and model risk management.
• Experience spanning Generative AI and at least one additional AI domain, such as classical ML, predictive analytics, NLP, computer vision, or intelligent automation.
• Experience with 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.
• Experience with modern deep learning and classical ML 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.
• Strength in technical communication, influence, developing others, and judgment under ambiguity.

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 their business impact and expected return.
• Design and implement major components of enterprise AI solutions across applications, integration, data, AI/ML, and infrastructure.
• Design and build AI solution patterns for machine learning and Generative AI, including LLM applications, RAG, 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, RBAC, secrets management, encryption, private networking, data protection, auditability, and authorization-aware data retrieval.
• Implement responsible-AI controls, including content safety, PII protection, bias identification and mitigation, prompt-injection defenses, and human review.
• Follow client data governance requirements and applicable data privacy regulation in work designed and delivered.
• 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; collaborate with delivery, data, infrastructure, and security functions.
• 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.

Benefits

• Potential eligibility for incentive compensation based on individual and/or company performance, depending on the position offered.
• Company benefits may include medical, dental, and vision insurance; flexible spending or health savings account; life and AD&D insurance; short- and long-term disability coverage; paid time off; employee assistance; participation in a 401(k) program with company match; and additional voluntary or legally required benefits.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior