Description

Define enterprise AI platform architecture, including control-plane services, SDKs, reusable templates, and developer tooling.

Design and implement model gateways supporting multi-provider routing, failover, rate limiting, tenant isolation, and cost tracking.

Build GenAI runtimes incorporating RAG, vector and hybrid search, context and memory stores, tool execution, and model integration.

Develop model, agent, prompt, and tool registries with versioning, approvals, lineage, and auditability.

Implement AI observability, distributed tracing, model evaluation, quality monitoring, latency tracking, and cost controls.

Establish responsible AI, security, privacy, guardrails, identity management, and policy enforcement.

Lead platform engineering using Kubernetes, infrastructure-as-code, CI/CD, cloud services, and reliability engineering.

Remain hands-on with production code, architecture reviews, technical standards, and build-versus-buy decisions.

Mentor and lead a team of approximately 3–8 platform engineers.

Partner with product leadership to drive AI platform roadmaps, adoption, reliability, and measurable business outcomes.

Required Qualifications

10–15+ years of software engineering, platform engineering, distributed systems, or infrastructure experience, including technical leadership.

Hands-on experience building and operating enterprise AI/GenAI platforms or comparable production ML, data, or developer platforms.

Strong Python skills and proficiency in TypeScript, Go, or JavaScript.

Deep knowledge of model gateways, RAG, agent platforms, AI evaluation, observability, traceability, and platform governance.

Experience with Kubernetes, infrastructure-as-code, CI/CD, cloud services, secrets management, secure SDLC, and production reliability.

Proven architecture ownership, cross-functional leadership, stakeholder communication, and engineering team development.

Bachelor's degree in a related field or equivalent professional experience.

Preferred Qualifications

Experience building multi-model AI platforms, agent registries, prompt management, evaluation frameworks, and GenAI tracing.

Background in healthcare, med-tech, life sciences, or other regulated enterprise environments.

Familiarity with HIPAA, GxP, PII/PHI protection, auditability, validation, and change control.

Experience with responsible AI, security policies, FinOps, MCP, OpenTelemetry, and cloud-native platform engineering

Key Skills

Education

Bachelor's degree