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About This Role
Job Summary
RadarFirst is seeking a Lead Platform Engineer, AI & DevSecOps Engineer to lead the next stage of our cloud, security, reliability, and AI operations maturity. This role will own the platforms, practices, and operating model that enable our engineering teams to build, deploy, monitor, secure, and continuously improve production SaaS systems and agentic AI capabilities.
Since launching RadarFirst's first agentic AI offering using AWS Bedrock, Amazon Bedrock AgentCore, and AgentCore Gateway, we are expanding the DevOps remit to include production-grade AI infrastructure and operations. The person in this role will help mature the environment required by customers in highly regulated industries, including governance, secure agent/tool access, observability, continuous LLM and agent evaluations, traceability, incident response, cost controls, and operational readiness for future agent development.
This is a hands-on technical leadership role. The right candidate is an experienced platform, DevSecOps, or SRE leader who can set direction, build shared engineering practices, mentor others, and create a more DevOps-forward engineering culture across historically siloed teams.
This position can be based out of our HQ in Portland, OR, or remote from anywhere in the U.S.
Our Technical Stack
Our front end is built with Angular and Material Design, using TypeScript for strong typing and maintainability. Backend services are written in Go and Nest.js.
Technologies currently in use include Packer, Ansible, Docker, Terraform, CloudFormation, Python, Go, Bash, Datadog, Git, TeamCity, Jira, Slack, and Google's productivity suite.
We deploy on AWS using EC2, ECS, RDS-hosted PostgreSQL, S3, OpenSearch, Lambda, SES, CloudWatch, IAM, and related AWS services. Our agentic AI environment uses AWS Bedrock, Amazon Bedrock AgentCore, and AgentCore Gateway, with a growing need for AI-specific observability, evaluation, governance, and operational automation.
Essential Responsibilities & Duties
Platform and Reliability
- Own and evolve the DevSecOps, platform engineering, and AI operations roadmap, with clear goals for reliability, security, deployment maturity, developer productivity, auditability, and customer readiness.
- Design, implement, and operate secure, scalable, fault-tolerant AWS infrastructure for SaaS and AI workloads.
- Implement automation to reduce manual deployment, provisioning, compliance, monitoring, and support work.
- Monitor platform KPIs and cost drivers, including cloud spend, token consumption, model usage, latency, error rates, deployment frequency, change failure rate, recovery time, and operational toil.
AI Operations
- Lead the operational maturity of RadarFirst's agentic AI platform, including the management of AWS Bedrock, AgentCore, AgentCore Gateway, agent/tool governance, policy controls, observability, evaluation workflows, and cost management.
- Build observability across infrastructure, applications, and agentic workflows, including logs, metrics, traces, token usage, latency, error rates, tool invocation behavior, evaluation results, and customer-impacting SLOs.
- Partner with Engineering, Product, Security, Compliance, and Customer-facing teams to ensure AI systems meet expectations for regulated industries, including auditability, access control, privacy, traceability, incident response, and defensible governance.
- Define and evolve CI/CD, infrastructure-as-code, and release-management practices that improve speed, safety, reproducibility, and rollback confidence.
Security and Governance
- Lead incident response and reliability improvement efforts, including post-incident reviews, root-cause analysis, alert tuning, runbooks, operational readiness reviews, and long-term remediation.
- Maintain and improve security practices across cloud infrastructure, CI/CD, secrets management, authentication, authorization, vulnerability scanning, and software supply chain controls.
Leadership and Culture
- Create shared engineering standards and paved paths that help product engineers ship safely without waiting on bespoke DevOps support.
- Drive a DevOps-forward engineering culture by reducing team silos, improving ownership models, clarifying operational responsibilities, and coaching teams on secure, observable, supportable software delivery.
- Mentor DevOps, platform, and software engineers; help develop future technical leaders and raise the organization's operational bar. Opportunity exists for people management for a small team.
Qualifications
- 10+ years of experience in DevOps, DevSecOps, SRE, platform engineering, cloud infrastructure, or a closely related engineering role.
- Proven track record designing and operating production AWS infrastructure for SaaS or enterprise software environments.
- Experience leading technical strategy, mentoring engineers, influencing cross-functional teams, and improving engineering culture.
- Strong hands-on experience with infrastructure as code, CI/CD, containerization, configuration management, scripting, and operational automation.
- Experience with AWS services such as EC2, ECS, IAM, RDS, S3, Lambda, CloudWatch, OpenSearch, and related networking and security services.
- Practical understanding of secure SDLC, application security, privacy, compliance, auditability, and risk management in regulated customer environments.
- Experience with observability platforms and practices, including metrics, logs, traces, SLOs, alerting, dashboards, and incident response.
- Familiarity with AI infrastructure, LLMOps, AgentOps, or MLOps concepts, especially production monitoring, evaluations, governance, cost optimization, and safe release practices for non-deterministic systems.
- Strong understanding of secrets management, certificates, authentication, authorization, encryption, and cloud security controls.
- Experience with vulnerability management and security tooling such as SCA, SAST, DAST, IAST, RASP, penetration testing, and security monitoring.
- Excellent communication, collaboration, leadership, analytical, and troubleshooting skills.
Strong Plus
- Hands-on experience with AWS Bedrock, Amazon Bedrock AgentCore, AgentCore Gateway, or comparable agentic AI platforms.
- Experience implementing LLM or agent evaluation frameworks, including automated regression tests, golden datasets, human review workflows, prompt/version management, safety checks, and release thresholds.
- Experience building governance for agent tool access, including policy enforcement, identity, permissions, audit trails, redaction, approval flows, and controlled access to internal systems.
- Experience with OpenTelemetry, CloudWatch, Datadog, Langfuse, Arize/Phoenix, Promptfoo, TruLens, OpenAI Evals, or similar AI observability/evaluation tools.
- Experience with regulated customers in healthcare, insurance, financial services, government, or privacy/security-sensitive industries.
- SaaS scale-up experience.
- Certifications or training such as SANS/SEC-540, SSCP, CISSP, CAP, CSSLP, HCISPP, AWS Certified DevOps Engineer, AWS Certified Security Specialty, or AWS Certified Machine Learning Specialty.
Research shows that people who identify as being from underrepresented groups are more likely to doubt the strength of their qualifications, so we encourage you to submit an application if you're interested in this role despite any reservations you may have about your background or skill set.
Who We Are
At RadarFirst, our mission is to make regulatory risk and data privacy simple, actionable, and sustainable.
We're transforming how organizations handle incidents and compliance with automated, purpose-built SaaS solutions. Recognized as pioneers in privacy, we've earned patents, industry awards, and the confidence of some of the world's most highly regulated industries, from healthcare and insurance to finance and beyond.
Our Values
- Build What's Next
- Be Good People
- Own the Outcome
- Customer Success is Our Success
- True Transparency
Why Join RadarFirst?
At RadarFirst, our team is filled with smart, thoughtful, and forward-thinking contributors who are experts at what they do. Our culture of innovation and trust is paramount to our success. We work hard, but we also encourage and support a healthy work/life balance. We offer a generous package of benefits and perks that make RadarFirst a great place to work, including:
- Comprehensive benefits that include medical and dental, 401k, Life and Disability insurance, generous flexible time off policy, paid holiday time, and 12 weeks paid parental leave. Plus flexible spending accounts for medical, dependent care, and commuter expenses
- Community outreach programs to encourage giving back to our community both as a group and individually
- Commitment to anti-racism work and accountability to our short-term and long-term equity & inclusion action plan
RadarFirst is a community-first organization, operating on a hybrid model. We actively support all employees working in the way they need. For those who wish to work from the office, these are some features of our downtown Portland office:
- On-site amenities such as indoor bike racks, showers, lockers, and gym facilities
- Casual work environment in an ideal central location, close to great food, shopping, and transportation options
This employer participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S. If E-Verify cannot confirm that you are authorized to work, this employer is required to give you written instructions and an opportunity to contact Department of Homeland Security (DHS) or Social Security Administration (SSA) so you can begin to resolve the issue before the employer can take any action against you, including terminating your employment. Employers can only use E-Verify once you have accepted a job offer and completed the Form I-9.
The salary range for this role is $155,000 - $175,000
#LI-REMOTE
Salary Context
This $155K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At RadarFirst, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($165K) sits 22% below the category median. Disclosed range: $155K to $175K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
RadarFirst AI Hiring
RadarFirst has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $175K - $175K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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