Note: The job is a remote job and is open to candidates in USA. Cook Medical is a company focused on providing innovative medical solutions, and they are seeking an Artificial Intelligence Solutions Engineer 2. In this role, you will design, build, and deliver production-grade AI systems, taking on responsibilities for project ownership, peer coaching, and collaboration across the organization.
Responsibilities
- Own discovery and delivery. Partner with internal and customer stakeholders to define problems, success metrics, and delivery plans; operate with greater independence to scope and execute AI projects end-to-end with limited direction
- Apply advanced data science techniques. Design and implement supervised/unsupervised learning, statistical modeling, and feature engineering solutions; validate hypotheses and inform AI system architecture and evaluation strategies with reduced oversight
- Build and evolve production AI applications. Design and implement LLM assistants, RAG systems, agentic workflows, and intelligent automation; establish evaluation frameworks, guardrails, and human-in-the-loop processes that meet production quality standards
- Make data usable at scale. Develop robust pipelines and integrations across enterprise systems (APIs, databases, event streams); enforce data quality, lineage, and governance standards to enable reliable AI capabilities across the organization
- Ship full-stack software. Implement production-grade backend services (Python/Java/Node/C++ or similar) and user interfaces (TypeScript/React) that are reliable, maintainable, and solve real user problems with measurable value
- Operate in the cloud. Deploy and run services on AWS/Azure/GCP using containers and orchestration (Docker/Kubernetes), CI/CD pipelines, secrets management, monitoring, and observability; contributes to improving team-wide cloud practices
- Integrate with enterprise platforms. Connect securely to internal platforms and data sources; takes ownership for accelerating delivery and driving measurable outcomes for frontline teams through well-engineered integrations
- Engineer for safety and trust. Apply and champion secure-by-design practices: data privacy, access controls, model/feature monitoring, bias and risk assessment, incident response, and auditability for ML/LLM systems across team projects
- Measure impact and drive adoption. Instrument products, track adoption, quality, and ROI; document architectural decisions; lead change management through demos, training sessions, and clear stakeholder communication
- Coach and mentor junior engineers and procurement team. Provide technical guidance, conduct code and design reviews, and actively support the development of AISE 1 team members and other junior contributors. Serve as a go-to technical resource for the team
- Lead project ownership. Take end-to-end accountability for complete AI/ML projects within the technical domain; delegate work components appropriately and ensure quality of team deliverables. Communicate progress and risk to leadership
Skills
- Bachelor's degree in Computer Science, Engineering, Mathematics, or related field—or equivalent practical experience with strong software engineering fundamentals
- 5+ years building production software or data/ML 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 complete AI/ML solutions independently with measurable business impact
- Experience coaching or mentoring junior technical colleagues is preferred
- Advanced proficiency in two or more of: Python, Java, C++, Go, TypeScript/JavaScript; strong command of testing frameworks, code review practices, and CI/CD
- Deep, hands-on experience with LLM application patterns (RAG, agents, tool-calling), vector databases, model evaluation/monitoring, and deployment of AI systems to production at scale
- Demonstrated ability to work directly with business stakeholders—driving discovery, scoping MVPs, presenting to leaders, and iterating on feedback—with limited oversight
- Advanced knowledge of at least one AI/ML technical specialty (e.g., LLM systems, data engineering, MLOps, AI security/safety) and practical awareness of adjacent specialties
- Experience with cloud infrastructure (AWS/Azure/GCP), containerization (Docker/Kubernetes), and production monitoring/observability tooling
- Background integrating with enterprise data/AI platforms in operational domains where AI augments frontline workflows
- Experience defining product and business health metrics and communicating trade-offs to technical and non-technical audiences
- Practical knowledge of project management methodologies (Agile/Scrum)
Company Overview
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