Artificial Intelligence Solutions Engineer 2

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)

Employee Requirements
Compliance with all policies of the company including without limitation the Employee Manual/Handbook (both local and/or regional). Code of Conduct, Electronic Information Policy, HIPAA regulations, Non Competition and Confidentiality Agreement."