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 lifecycleblending applied data science data and ML engineering and full-stack application developmentand 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

Bachelors degree in Computer Science Engineering Mathematics or related fieldor 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 (Masters or PhD) may reduce this requirement by 24 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 stakeholdersdriving discovery scoping MVPs presenting to leaders and iterating on feedbackwith 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.