What are the responsibilities and job description for the Artificial Intelligence Solutions Engineer position at Cook Medical?
Overview
At Cook Medical, artificial intelligence (AI) is more than a technology—it’s a strategic enabler of innovation and patient-focused solutions. We integrate AI responsibly across our operations to enhance efficiency, personalize experiences, and unlock insights that improve healthcare outcomes. Our approach is guided by Cook’s core values of integrity, transparency, and respect, ensuring every AI initiative aligns with ethical standards and regulatory compliance.
AI Solutions Engineers turn ambiguous, high-value business problems into production-grade AI systems. This role covers the full lifecycle— blends of data science, data/ML engineering, and application development to deliver tools across the global enterprise. This position requires close collaboration with both technical and non-technical partners.
Responsibilities
Education and/or Work Experience Qualifications
At Cook Medical, artificial intelligence (AI) is more than a technology—it’s a strategic enabler of innovation and patient-focused solutions. We integrate AI responsibly across our operations to enhance efficiency, personalize experiences, and unlock insights that improve healthcare outcomes. Our approach is guided by Cook’s core values of integrity, transparency, and respect, ensuring every AI initiative aligns with ethical standards and regulatory compliance.
AI Solutions Engineers turn ambiguous, high-value business problems into production-grade AI systems. This role covers the full lifecycle— blends of data science, data/ML engineering, and application development to deliver tools across the global enterprise. This position requires close collaboration with both technical and non-technical partners.
Responsibilities
- Own discovery and delivery. Partner with internal and customer stakeholders to define problems, success metrics, and delivery plans; iterate rapidly based on user feedback.
- Build production AI applications. Design and implement solutions such as LLM assistants, retrieval-augmented generation (RAG) systems, and intelligent automation—including prompt design, evaluation frameworks, guardrails, and human-in-the-loop workflows.
- Make data usable. Develop pipelines and integrations across enterprise systems (APIs, databases, event streams); perform modeling, quality checks, lineage, and governance to enable AI at scale.
- Ship full-stack software. Implement reliable backend services (Python/Java/Node/C or similar) and practical user interfaces (TypeScript/React) that solve real user problems.
- Operate in the cloud. Deploy and run services on AWS/Azure/GCP with containers/orchestration (Docker/Kubernetes), CI/CD, secrets management, monitoring, and observability.
- Integrate with enterprise platforms. Connect securely to internal platforms and data sources to accelerate delivery and measurable outcomes for frontline teams.
- Engineer for safety and trust. Apply secure-by-design practices: data privacy, access controls, model/feature monitoring, bias and risk assessment, incident response, and auditability for ML/LLM systems.
- Measure impact and drive adoption. Instrument products, track adoption/quality/ROI, document decisions, and lead change management through demos, training, and clear communication.
- Lead with ownership. Work in small, agile teams; communicate effectively with executives, domain experts, and engineers; take responsibility for high-impact projects.
Education and/or Work Experience Qualifications
- Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field—or equivalent practical experience with strong software engineering fundamentals.
- 3 years building production software or data/ML systems (e.g., full-stack, data engineering, MLOps, or platform engineering).
- Proficiency in at least two of: Python, Java, C , Go, TypeScript/JavaScript; familiarity with testing, code review, and CI/CD.
- Hands-on experience with one or more of: LLM application patterns (including RAG), vector databases, model evaluation/monitoring, and deployment of AI systems to production.
- Direct experience working with end users/customers—driving discovery, scoping MVPs, presenting to leaders, and iterating in fast feedback loops.
- Background integrating with enterprise data/AI platforms and operational domains where AI augments frontline workflows.
- Experience defining product and business health metrics and communicating trade-offs to technical and non-technical audiences.
- Familiarity with regulated environments (e.g., healthcare, life sciences) is a plus.
- 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