The AI Solutions Engineer 2 is a seasoned technical individual contributor who operates with significant autonomy to design, build, and deliver production-grade AI systems that address high-value business challenges across the global enterprise. This role covers the full AI/ML lifecycle—blending applied data science, data and ML engineering, and full-stack application development—and takes on expanded responsibility for technical project ownership, peer coaching, and cross-functional collaboration.

The AISE 2 works independently under limited supervision, contributes meaningfully to departmental outcomes, and serves as a technical resource (coaching practical use of the active AI tools).

• 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.

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.• 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.

Knowledge, Skills, and Abilities

• 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.

Preferred Qualifications:• 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).

Physical Requirements• Works under general office environmental conditions• Some travel may be required (e.g., for team-on-sites, professional development, or deployment support)