Managing Director, AI GTM Solution Architect Lead

NTT DATA Services•Brisbane, CA

2d•$150,000 - $350,000

About The Position

NTT DATA is seeking an experienced Managing Director, AI GTM Solution Architect Lead to lead the design and architecture of scalable, secure, production-grade AI solutions that address complex business problems. The position combines technical leadership, hands-on architecture, and attention to regulatory considerations, with a focus on Generative AI and machine-learning-based analytical solutions. The role defines end-to-end Agentic AI, AI/ML, and Generative AI architectures aligned with business objectives and enterprise standards. It designs agentic AI use cases, architects LLM-based solutions, evaluates models and platforms, and develops scalable machine-learning solutions. The role also oversees feature engineering, model lifecycle management, MLOps, and monitoring, while ensuring performance, explainability, fairness, and regulatory requirements are met. The Managing Director architects cloud-based AI solutions and data architectures, guides engineering teams on scalable and secure implementations, and establishes AI governance, security, and responsible-AI practices. The position serves as a technical thought leader, mentors technical teams, supports pre-sales and client workshops, and keeps current with AI and insurance technology trends. The position is based in San Francisco, California. NTT DATA hires locally where possible, and in-office attendance near an office or client site may be required for meetings or events depending on business needs. Remote or hybrid arrangements may be available but are subject to change based on client requirements.

Requirements

Strong experience designing Generative AI solutions using LLMs such as OpenAI, Azure OpenAI, Anthropic, or LLaMA.
Hands-on expertise in machine learning, NLP, deep learning, and statistical modeling.
Experience with RAG architectures and vector databases such as Pinecone, FAISS, or Weaviate.
Proficiency in Python and machine-learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Experience with MLOps tools and CI/CD pipelines.
Experience with at least one cloud platform: AWS, Azure, or GCP.
Proven experience as a Solution Architect or AI Architect in enterprise environments.
Strong stakeholder communication and documentation skills.
Ability to bridge business, data science, and engineering teams.
Strategic thinking and a hands-on execution mindset.

Nice To Haves

Cloud certifications.
Advanced degree in Computer Science, Data Science, AI, or a related field; PhDs in statistics are preferred.
Experience delivering AI solutions for large insurance enterprises.
Exposure to Responsible AI, model explainability tools such as SHAP or LIME, and governance frameworks.
Experience with document AI, OCR, speech analytics, or conversational AI in insurance.

Responsibilities

Define end-to-end Agentic AI, AI/ML, and Generative AI solution architectures aligned with business objectives and enterprise standards.
Design and implement agentic AI use cases, including underwriting assistance, claims automation, document intelligence, customer engagement, fraud detection, and agent productivity.
Architect LLM-based solutions, including RAG pipelines, prompt engineering strategies, fine-tuning approaches, and orchestration frameworks.
Evaluate and recommend LLMs, embedding models, vector databases, and open-source or commercial GenAI platforms.
Design scalable machine-learning solutions for predictive analytics, risk modeling, pricing, churn prediction, fraud detection, and customer segmentation.
Oversee feature engineering, model lifecycle management, MLOps pipelines, and model monitoring strategies.
Ensure AI solutions meet performance, explainability, fairness, and regulatory requirements.
Architect AI solutions on AWS, Azure, or GCP, using native AI/ML services where appropriate.
Define data architectures that integrate structured and unstructured data.
Guide engineering teams on scalable APIs, microservices, event-driven architectures, and secure deployments.
Establish best practices for AI governance, security, and responsible AI.
Act as a technical thought leader for AI engagements.
Mentor data scientists, ML engineers, and solution architects.
Support pre-sales, solutioning, and client workshops with technical credibility.
Stay current with emerging AI, GenAI, and insurance technology trends.

Benefits

Medical insurance with an employer contribution.
Dental insurance with an employer contribution.
Vision insurance with an employer contribution.
Flexible spending or health savings account.
Life and AD&D insurance.
Short- and long-term disability coverage.
Paid time off.
Employee assistance.
401(k) program with company match.
May be eligible for incentive compensation based on individual and/or company performance.