AI Technical Architect, Manager Consulting

About the Role

We are seeking a AI Architect who will lead the design, deployment, and integration of advanced AI/ML solutions for real‑time manufacturing supply chain systems. This role requires deep expertise in technical architecture, hands‑on experience with LLMs and AI agent frameworks, and strong leadership to ensure secure, scalable, and high‑impact AI platforms that drive operational excellence and innovation.

In this role you will Solution Architecture & Technical Leadership

• Lead the end‑to‑end design and architecture of AI/ML solutions, ensuring alignment with manufacturing and supply chain business objectives.
• Architect scalable, secure, and robust AI‑driven platforms, leveraging AWS cloud infrastructure, GPU environments, and enterprise‑grade networking and storage.
• Oversee the integration of LLMs (e.g., GPT, Bedrock) and AI agent frameworks (such as CrewAI) into existing and new supply chain systems.

AI/ML Solution Design & Deployment

• Design, develop, and deploy AI/ML models, focusing on real‑time data processing, anomaly detection, and predictive analytics for supply chain optimization.
• Implement prompt engineering best practices and optimize LLM‑based workflows for high‑fidelity, context‑aware responses.
• Ensure seamless integration of AI agents with APIs, data sources, and user interfaces (e.g., chatbots, dashboards).

Cloud & Infrastructure Management

• Define and manage cloud architecture on AWS, including compute, storage, networking, and GPU provisioning for model training and inference.
• Oversee CI/CD pipelines, model registry, monitoring, logging, and AI lifecycle management to support continuous delivery and operational excellence.
• Collaborate with IT and Dev Ops teams to ensure infrastructure scalability, reliability, and cost‑effectiveness.

Security, Compliance & Data Governance

• Ensure all AI solutions comply with enterprise security standards, data privacy regulations (e.g., CCPA), and internal governance policies.
• Implement end‑to‑end encryption, access controls, and audit mechanisms for sensitive manufacturing and supply chain data.
• Collaborate with governance, cyber, and compliance teams to address risks and maintain regulatory alignment.

Stakeholder Engagement & Communication

• Serve as the primary technical point of contact for onsite and remote stakeholders, including business leaders, IT, and vendor partners.
• Translate complex technical concepts into clear, actionable insights for both technical and non‑technical audiences.
• Lead technical workshops, architecture reviews, and solution demos to drive stakeholder alignment and adoption.

Performance, Risk & Quality Management

• Define and track KPIs for AI solution accuracy, efficiency, and user adoption; leverage dashboards and analytics for continuous improvement.
• Identify and mitigate technical risks, including model drift, data quality issues, and infrastructure bottlenecks.
• Ensure robust testing, validation, and documentation of all AI/ML components and workflows.

Team Leadership & Knowledge Sharing

• Mentor and guide onsite and remote team members, including data scientists, engineers, and prompt engineers.
• Foster a culture of innovation, collaboration, and continuous learning within the AI delivery team.
• Stay current with advancements in AI/ML, cloud, and supply chain technologies, and drive adoption of best practices.

Work Model

We believe hybrid work is the way forward as we strive to provide flexibility wherever possible. Based on this role’s business requirements, this is a hybrid position requiring 3 days a week in a client or Cognizant office in Plano, Texas. Regardless of your working arrangement, we are here to support a healthy work‑life balance through our various wellbeing programs.

What you need to have to be considered

• 12+ years in technical architecture, with 3+ years in AI/ML solution design and deployment.
• Proven experience in AI‑driven platforms for manufacturing or supply chain.
• Hands‑on with LLMs (e.g., GPT, Bedrock), prompt engineering, and AI agent frameworks (CrewAI).
• Strong knowledge of AWS cloud, compute, storage, networking, and GPU environments.
• Experience with CI/CD, model registry, monitoring/logging, and AI lifecycle…