Applied AI Engineer (Enterprise) - Healthcare & Life Sciences

Quick Facts

• Role: Applied AI Engineer (Enterprise)

• Domain: Healthcare & Life Sciences (HLS)

• Focus: Build, evaluate, and deploy production AI systems

Description

You will partner with enterprise organizations in Healthcare and Life Sciences to turn AI use cases into durable, secure, production-ready systems and enable scaled adoption. You will lead hands-on engineering and make technical decisions across model behavior, reliability, latency, cost, safety, security, governance, and operational readiness. Your work spans clinical, operational, administrative, and research workflows while translating security, privacy, and governance needs into practical deployment choices.

Responsibilities

• Design, build, evaluate, and deploy AI systems from prototype to reliable production

• Translate high-value customer opportunities into architectures, implementation plans, evaluation strategies, and success criteria

• Make technical decisions across model selection, agent/tool design, retrieval/context design, data pipelines, evaluation, reliability, observability, latency, cost, safety, security, and governance

• Diagnose complex issues by reproducing failures, testing hypotheses, and removing blockers

• Collaborate with customer engineering teams and internal product/research/security/engineering/go-to-market partners to feed deployment learnings back into product improvements

• Implement responsible AI deployments with security, privacy, clinical, and business stakeholders (data handling, access controls, evaluation, human oversight, monitoring)

• Create reusable architectures, tooling, playbooks, and technical guidance to accelerate future deployments

Requirements

• Personally designed, built, and shipped AI/ML systems in enterprise environments from prototype to production

• Highly proficient in Python and comfortable working end to end across model APIs, agent/tool development, retrieval, data pipelines, evaluation, observability, integrations, and production operations

• Experience evaluating AI systems using representative data, automated graders, and human judgment

• Experience with enterprise production needs: reliability, observability, security, privacy, data governance, performance, and cost

• Delivered AI/software in healthcare, life sciences, or similarly regulated enterprises with measurable outcomes beyond proof of concept; familiarity with sensitive data, legacy systems, and interoperability

• Clear communication with engineers, technical leaders, security/product teams, and executives

• End-to-end ownership and strong technical judgment in ambiguous settings

• Healthcare or life sciences experience is strongly preferred; other regulated enterprise backgrounds are also considered

Benefits

• Hybrid work option (3 days per week in office) in New York City or San Francisco, or remote work

• Relocation assistance for eligible new hires