Director of Projects - Engagement Delivery Lead

Cognizant; Natoma (CA); Hybrid; USD 250,000 - 420,000; Full time

Job summary

Cognizant is seeking a Chief Architect to lead enterprise AI architecture, guiding generative AI and ML integration across complex platforms. Role emphasizes scalable architectures, ethical AI usage, and strategic alignment with long-term organizational goals.

Leadership responsibilities include governance, collaboration with diverse teams, and defining non-functional requirements for robust AI systems across hybrid environments.

Qualifications

• Twenty years of designing large-scale enterprise architectures with AI/ML in real-world business contexts.
• Hands-on expertise in generative AI: architectures prompting techniques fine-tuning deployment strategies across cloud/native or on-premises.
• Deep understanding of AI/ML foundations: supervised/unsupervised learning, model evaluation, optimization, data prep.

Responsibilities

• Lead enterprise-wide AI architecture definition embedding generative AI and ML into core platforms for scalable, resilient solutions.
• Create AI solution blueprints detailing data flows, lifecycles, services, and integration patterns for reusability across hybrid environments.
• Oversee governance standards, design principles, and review processes ensuring robustness, efficiency, and responsible AI practices.
• Guide model selection, training, deployment, optimization for secure data handling and transparent evaluation of performance and bias.
• Coordinate with data science, engineering, security, and business stakeholders to translate analytics needs into practical AI architectures.
• Develop reference architectures for AI workloads including model orchestration, feature stores, experiment tracking, and monitoring.
• Evaluate emerging AI/ML technologies and provide architectural recommendations balancing innovation, cost, and readiness for large multinational environments with no travel expectations.
• Define non-functional requirements for AI systems such as scalability, latency, reliability, observability, and regulatory adherence.
• Collaborate on data pipelines and model serving infrastructure supporting training, validation, and inference with privacy considerations.
• Document architectural decisions and operational guidelines to keep AI platforms understandable and auditable.
• Mentor teams on architecture best practices and reusable components to improve customer experiences and business outcomes.
• Review complex AI implementations and provide feedback addressing security, resilience, and sustainability without adding unnecessary complexity or travel.
• Influence roadmaps for scaling generative AI responsibly across markets to improve efficiency, personalization, and social value.

Skills

AI architecture

Generative AI

Model deployment

Governance

Collaboration

Responsible AI

Hybrid work

Enterprise-scale

Regulatory awareness

Job description

Job Summary

Serve as Chief Architect for enterprise grade artificial intelligence solutions in a hybrid work model guiding the strategic design and integration of generative AI and machine learning capabilities across complex business platforms. Apply deep expertise in AI and ML concepts to create scalable architectures that optimize performance reliability and ethical AI usage while aligning technology outcomes with long term organizational goals and measurable value for customers and communities.

Responsibilities

• Lead the end to end definition of enterprise wide artificial intelligence architecture that embeds generative AI and machine learning capabilities into core business platforms to deliver resilient and scalable solutions that advance strategic objectives and societal impact.
• Drive the creation of comprehensive AI solution blueprints that detail data flows model lifecycles inference services and integration patterns enabling product and engineering teams to implement consistent and reusable components across hybrid work environments.
• Oversee architectural governance for AI and ML systems by defining standards design principles and review processes that ensure technical robustness operational efficiency and ethical alignment with responsible artificial intelligence practices.
• Provide expert guidance on generative AI model selection training deployment and optimization ensuring that architectures support secure data handling controlled experimentation and transparent evaluation of model performance and bias.
• Coordinate closely with data science engineering security and business stakeholders to translate complex analytical requirements into practical AI architectures that can be maintained monitored and evolved within day shift operational constraints.
• Develop reference architectures and patterns for artificial intelligence workloads including model orchestration feature stores experiment tracking and monitoring so that teams can accelerate delivery while managing technical debt and long term maintainability.
• Evaluate emerging technologies frameworks and platforms in the AI and ML ecosystem and produce objective architectural recommendations that balance innovation risk cost and operational readiness for large multinational environments with no travel expectations.
• Guide the definition of non functional requirements for AI systems such as scalability latency reliability observability and compliance ensuring that generative AI solutions integrate smoothly with existing enterprise applications and data platforms.
• Collaborate on the design of data pipelines and model serving infrastructure that support robust training validation and inference workflows leveraging hybrid access patterns while maintaining data privacy and regulatory adherence.
• Support teams in documenting architectural decisions design rationales and operational guidelines so that AI platforms remain understandable auditable and adaptable for future enhancements and cross functional collaboration.
• Champion effective use of AI and ML concepts by mentoring teams on architecture best practices and by shaping reusable components that help products deliver measurable improvements in customer experiences and business outcomes.
• Review complex AI solution implementations and provide constructive architectural feedback that addresses security resilience performance and sustainability concerns without introducing unnecessary complexity or travel dependencies.
• Influence organizational roadmaps by articulating how generative AI and artificial intelligence capabilities can be responsibly scaled across markets thereby improving operational efficiency product personalization and social value through thoughtful technology choices.

Qualifications

• Bring extensive experience of at least twenty years in designing large scale enterprise architectures with a strong focus on applying artificial intelligence and machine learning concepts to real world business problems.
• Demonstrate advanced hands on expertise in generative AI including model architectures prompting techniques fine tuning approaches and deployment strategies across cloud native or on premises environments.
• Show deep understanding of core AI and ML foundations such as supervised and unsupervised learning model evaluation optimization techniques and data preparation practices relevant to complex organizational datasets.
• Apply practical knowledge of artificial intelligence platforms and tools used for experimentation model lifecycle management and monitoring while ensuring solutions remain secure robust and aligned with governance expectations.
• Exhibit strong experience collaborating with multidisciplinary teams including data scientists software engineers product owners and risk partners to turn conceptual AI initiatives into sustainable architectures and operational solutions.
• Possess familiarity with responsible AI principles including fairness transparency explainability and privacy and be able to embed these practices directly into architectural standards and design patterns.
• Demonstrate comfort working in a hybrid model and day shift context using modern collaboration tools and documentation practices to maintain architectural consistency and clear communication across distributed teams.
• Prefer experience with large multinational organizations where complex regulatory landscapes data residency considerations and high reliability expectations shape the overall design of AI platforms and solutions.