Senior Artificial Intelligence Solutions Engineer

Overview

The Senior AI Solutions Engineer is a recognized technical subject matter expert who leads large, complex AI/ML initiatives across the enterprise with limited oversight. This role combines deep technical expertise in AI systems engineering with the ability to manage major cross-functional projects, shape technical strategy, and develop the capabilities of the broader AI Solutions team. The Senior AISE contributes to the definition of departmental goals and technical direction, influences AI practices and standards across the organization, and serves as the most senior individual contributor within the AI Solutions Engineer career ladder. Working across the full AI/ML lifecycle-from discovery and data architecture through production delivery and sustained impact measurement-the Senior AISE is accountable for major impact on business and departmental results.

Responsibilities

• Lead discovery and strategic scoping. Serve as the primary technical authority partnering with senior business stakeholders to define high-value AI opportunities, establish success metrics, and create delivery roadmaps; set the standard for how AI projects are structured and executed across the team.
• Architect advanced AI and data science solutions. Design and oversee implementation of complex AI/ML systems-including LLM applications, multi-modal AI, agentic frameworks, and statistical modeling-at an architectural level, ensuring scalability, reliability, and measurable enterprise impact.
• Drive production AI quality and standards. Establish and maintain evaluation frameworks, guardrails, human-in-the-loop processes, and quality standards for AI systems across the team. Define best practices for prompt engineering, model fine-tuning, and AI system monitoring.
• Lead enterprise data and AI platform integration. Own the architecture and delivery of complex data pipelines, integrations, and AI platform connections across enterprise systems; define data governance standards and lineage requirements for AI at scale.
• Deliver full-stack AI applications. Implement or oversee delivery of production-grade backend services and user interfaces; maintain high engineering standards and actively drive code quality across team deliverables through architecture reviews and technical guidance.
• Champion cloud-native AI operations. Establish cloud architecture patterns (AWS/Azure/GCP) and best practices for AI workloads; own the design of scalable deployment, orchestration, monitoring, and observability infrastructure for the team's AI systems.
• Advance AI safety, trust, and governance. Lead the definition and adoption of secure-by-design AI practices: privacy-preserving design, access control models, responsible AI principles, bias mitigation, incident response protocols, and compliance with regulatory requirements.
• Contribute to technical strategy and goal development. Collaborate with the Director of AI Solutions and cross-functional leaders to shape the technical roadmap; contribute to departmental goal-setting, tool and platform selection decisions, and the evolution of AI engineering capabilities.
• Manage major AI projects and programs. Lead large, complex technical projects or programs spanning multiple teams or business units; define project structure, delegate work, manage technical risks, and ensure delivery of high-quality outcomes with limited management oversight.
• Develop team capabilities. Serve as senior mentor and technical coach across the AI Solutions team; conduct architecture and design reviews, lead internal knowledge-sharing sessions, and actively invest in the growth of AISE 1 and AISE 2 engineers.
• Communicate AI value to leadership. Translate complex technical outcomes into clear business narratives for executive and cross-functional audiences; lead change management efforts, drive adoption of AI tools, and represent the AI Solutions function in strategic conversations.

Qualifications

Education and/or Work Experience Qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics, or related field-or equivalent practical experience with exceptional software engineering and AI/ML fundamentals.
• 7+ years building and delivering production AI/ML or software systems (e.g., full-stack, data engineering, MLOps, or platform engineering). Advanced degrees (Master's or PhD) may reduce this requirement by 2-4 years.
• Demonstrated track record of delivering large, complex AI/ML systems that generated significant and measurable business impact across multiple teams or business units.
• Recognized as a technical expert in at least one AI/ML domain, with evidence of influencing technical strategy or standards at an organizational level.
• Experience leading or managing technical projects or programs with multiple contributors.

Knowledge, Skills, and Abilities

• Expert-level proficiency in two or more of: Python, Java, C++, Go, TypeScript/JavaScript; mastery of testing, code review, CI/CD, and software engineering best practices at scale.
• Deep expertise in LLM application patterns (RAG, agents, multi-modal, fine-tuning), vector databases, model evaluation/monitoring, and large-scale deployment and governance of AI systems in production.
• Strong applied data science skills including statistical modeling, feature engineering, experiment design, and hypothesis validation in production environments.
• Demonstrated ability to architect, scope, and deliver complex AI solutions with limited direction; ability to anticipate technical risks and design for robustness, scalability, and long-term maintainability.
• Expert-level knowledge of cloud infrastructure (AWS/Azure/GCP), containerization (Docker/Kubernetes), MLOps platforms, and AI monitoring/observability at scale.
• Recognized specialty in at least one AI/ML discipline; awareness of advances within the specialty and ability to evaluate emerging approaches for enterprise applicability.
• Strong practical knowledge of project management methods (Agile/Scrum/SAFe); experience managing technical complexity across multiple contributors.

Preferred Qualifications:

• Experience contributing to or defining AI/ML technical strategy at a team or organizational level.
• Background in enterprise data/AI platform integration within regulated operational domains (healthcare, life sciences, medical devices); familiarity with FDA, HIPAA, and ISO requirements related to AI/ML systems.
• Experience presenting AI strategies, capabilities, and ROI to executive leadership.
• Track record of growing other engineers through structured mentorship, architecture guidance, or community-of-practice leadership.

Physical Requirements

• Works under general office environmental conditions.
• Some travel may be required (e.g., for team on-sites, professional development, or deployment support).
• Personal protective equipment including safety glasses, lab coat and gloves required in many areas associated with this position.
• Must be able to perform the essential functions of the job, subject to reasonable accommodation requirements under the ADA.

Qualified candidates must be legally authorized to be employed in the United States. Cook does not intend to provide sponsorship for employment visa status (e.g., H-1B or TN status) of this employment position.