Senior AI Engineer

You’ll deliver practical AI capabilities on top of our existing engineering and operations foundation (CI/CD, code quality, and observability) with strong guardrails for HIPAA/GxP and FedRAMP-boundary work.

Tech environment (varies by team): Java, Python, Go, TypeScript; AWS; GitLab/Jenkins/GitLab CI; SonarQube; Prometheus + ELK and AWS-native monitoring.

Build and operate an internal AI platform for developer workflows: code assistance, PR review support, test generation, documentation, and automation that fits how our teams work.

Integrate AI capabilities into the toolchain (CI/CD, code quality, repos) and make adoption easy through templates, examples, and self-service patterns.

Implement AI quality gates (policy checks, confidence thresholds, regression tests, secure-by-default configs) so AI-generated changes cannot be merged/released unless they meet standards.

Design for regulated environments: implement logging/auditability, access controls, and data-handling guardrails for HIPAA/GxP work.

Maintain a FedRAMP-boundary-safe approach for SBP Federal work (separation where required; evaluate compliant alternatives; prevent prohibited tooling/data flows).

Partner with engineering leads and security/compliance to define and roll out internal standards for responsible AI use (including review practices and acceptable-use guidance).

Build AI-assisted operations on top of existing observability signals: anomaly detection, incident triage support, runbook-driven automation, and self-healing patterns to reduce MTTR.

Measure impact (adoption, cycle time, incident metrics), iterate quickly, and communicate progress clearly to engineering leadership.

5+ years in software engineering, platform engineering, DevEx, or SRE/operations engineering, with recent hands-on work applying LLMs/AI to real workflows.

Experience building or productionizing AI-assisted developer tooling (e.g., code review assistance, test generation, documentation, workflow automation)—not just evaluating off-the-shelf tools.

Experience building and operating automated/semi-automated agent workflows that execute scoped engineering task

Experience building AI-assisted operations/observability capabilities (anomaly detection, intelligent alerting, triage automation, runbook automation) on top of existing monitoring stacks.

Strong coding skills in Python plus one of Java or TypeScript.

Comfort operating in regulated environments and designing guardrails (HIPAA, GxP, FedRAMP, or similar), including boundary/tenant separation and auditability.

Clear written and verbal communication; able to align engineers, security/compliance, and leadership.

Preferred

Experience with FedRAMP boundaries and evaluating FedRAMP-authorized tooling alternatives.

Experience integrating deeply with CI/CD and developer toolchains (GitHub/Jenkins/GitLab CI, SonarQube, CodeBuild, etc.).

Prior platform/DevEx experience building internal products where engineers are the customers.

Experience in regulated software domains (clinical, federal, financial, or similar).

Familiarity with AWS-native ops tooling (CloudWatch, DevOps Guru) and workflow/orchestration tools.

Success in this role looks like measurable improvements in engineering throughput and operational outcomes (e.g., faster reviews, more reliable releases, reduced alert fatigue, lower MTTR) while meeting compliance requirements. If you like building internal platforms that teams rely on every day—and you can balance speed with strong guardrails—we’d like to talk.