Ambiguous, high-value business problems into production-grade AI systems for the global enterprise
Requirements
- 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.
- Experience with AWS/Azure/GCP, containers/orchestration (Docker/Kubernetes), CI/CD, secrets management, monitoring, and observability.
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.
Other
- 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).
- Some travel may be required (e.g., for team-on-sites, professional development, or deployment support)
- 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.
