Principal AI Operations Engineer (Remote)

$152K - $220K Remote Senior AI/ML Engineer

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Skills & Technologies

GcpPython

About This Role

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The Principal AI Operations Engineer reports to the Sr. Director, AI Center of Excellence, in the Technology Department. This role defines how AI systems at Inspira Financial are instrumented, measured, monitored, and evidenced, and drives adoption of those standards across the enterprise, spanning both vendor-provided and internally built AI capabilities. Primary duties include building the enterprise telemetry and observability standard for AI systems; designing evaluation, drift detection, and agent guardrail frameworks; translating AI risk and compliance frameworks into operational controls and audit evidence; maintaining the enterprise AI system inventory; and teaching other engineering teams to adopt these standards independently.

Duties & Responsibilities:

  • Define the enterprise telemetry standard for AI systems, covering traces, prompts, tool calls, retrieval, tokens, latency, and cost, built on a vendor-neutral schema such as OpenTelemetry GenAI semantic conventions.
  • Build the logging and observability layer that provides the AI Center of Excellence, business units, and the AI Governance Committee with a single view of AI activity across custom applications, agent platforms, and vendor-embedded AI.
  • Integrate AI telemetry with enterprise log aggregation, application performance monitoring, security monitoring, alerting, and incident response platforms to support operational visibility, investigation, and audit retention.
  • Establish retention, redaction, masking, and access patterns for AI conversation and trace data that satisfy privacy, records retention, and legal hold obligations.
  • Publish reference implementations and starter kits so teams adopting the telemetry standard inherit a working path.
  • Design the evaluation framework for AI systems, including golden datasets, offline evaluation harnesses, automated and LLM-as-judge scoring, and regression gates in CI.
  • Define drift criteria for each class of AI system and build detection for output quality degradation, retrieval decay, input distribution shift, and silent model updates from providers or vendors.
  • Build runtime guardrails and boundary enforcement for agents, including tool and action authorization, scope and spend limits, escalation paths, human-in-the-loop triggers, and hard-stop conditions.
  • Establish the AI incident model, including severity taxonomy, detection, alerting, rollback, postmortem process, and conversion of incidents into new test cases.
  • Translate NIST AI RMF, ISO/IEC 42001, model risk management practice, and applicable privacy and recordkeeping obligations into operational controls engineers can implement.
  • Build the evidence layer that automates capture of artifacts needed for internal audit, external examination, and client due diligence.
  • Integrate AI operational data with governance, risk, and compliance platforms to support continuous control monitoring, automated evidence collection, risk reporting, and audit readiness.
  • Maintain the operational AI inventory across custom builds, agents, integrations, and vendor AI features, including an owner, risk classification, and control coverage for each entry.
  • Define the assurance pattern for vendor AI, including required telemetry, attestations, detection of vendor model changes, and compensating monitoring.
  • Build metric frameworks connecting AI usage, quality, cost, and risk posture to business outcomes, and report these to CoE leadership, business unit leaders, and the AI Governance Committee.
  • Own token and platform cost visibility and attribution by team and use case and lead resulting optimization work.
  • Run working sessions, document patterns, review designs, and coach teams through their first fully instrumented AI deployment.
  • Partner with Risk, Compliance, Legal, Privacy, and Security to incorporate feedback from the risk triad and ensure adequate detection, response, and mitigation measures are in place.
  • Perform other duties as assigned.

Requirements: Education & Experience:

Minimum required:

  • Bachelor of Science in Engineering or Computer Science
  • Relevant certifications are a plus: Datadog Certified Professional, Google Cloud Professional Data Engineer for BigQuery ML, or PagerDuty’s AI Ops
  • 8 or more years of telemetry-driven engineering experience, including 2 years operating LLM, ML, or agentic systems in production, with hands-on ownership of instrumentation and monitoring.

Skills & Abilities:

  • Production-grade proficiency in Python and SQL, including building tooling, pipelines, and services other teams depend on.
  • Demonstrated experience in AI evaluation and observability, including eval harnesses, tracing, scoring, drift detection, and guardrails.
  • Working command of at least one AI risk or governance framework, such as NIST AI RMF, ISO/IEC 42001, EU AI Act, or model risk management practice, and the ability to convert it into controls and code.
  • Experience operating in a regulated environment where logging, retention, evidence, and access control are enforced rather than aspirational.
  • Ability to influence without formal authority across engineering teams, legal counsel, and executive stakeholders.
  • Financial services, healthcare, benefits administration, or other regulated industry experience preferred, particularly environments handling PHI, PII, or fiduciary data.
  • Depth of knowledge with telemetry platforms and related metric and measurement practices preferred.
  • Hands-on experience integrating and operating enterprise platforms for AI and application observability, centralized logging, governance, risk, continuous compliance monitoring, AI system inventory, evaluation and testing, incident management, and cloud cost management preferred.
  • FinOps or cloud cost management experience applied to token and inference spend preferred.
  • AI security fluency preferred, including prompt injection defense, OWASP LLM and agentic risk categories, red teaming, tool sandboxing, and least-privilege design for agent identities.
  • Track record of standing up a capability that other teams adopted voluntarily preferred.

Other Requirements:

  • Ability to occasionally work outside normal business hours to meet project deadlines as needed.
  • Prolonged periods of sitting at a desk and working on a computer.

Req Benefits: The compensation for this position will vary depending on factors such as your location, skills and experience. The compensation package may also include incentive and bonus opportunities.

Inspira provides industry-leading benefits including, but not limited to: healthcare, 401K savings plan, company holidays, paid time off, parental leave and an employee assistance program.

Compensation: $152,700-$220,000 per year

Additional Details : Take the next step in your journey at Inspira Financial. Guided by our mission to enable better health and greater wealth, we help businesses and individuals thrive today, tomorrow, and into retirement by strengthening and simplifying the health and wealth journey. Serving more than 7 million clients and representing over $62 billion in assets, Inspira partners with thousands of employers, plan sponsors, recordkeepers, TPAs, and other institutional partners to help the people they support plan, save, and invest for a brighter future. Through solutions including automatic rollover services, health savings accounts, emergency savings, and custody services, Inspira is focused on delivering better outcomes for all. Learn more at inspirafinancial.com. Our values shape how we show up every day. We Lead with Heart by acting with compassion, empathy, and doing what's right; Cultivate Trust through integrity, transparency, and accountability; Aim Higher by never settling for what's expected and continually raising the standard; and Win Together by collaborating inclusively to achieve the best outcomes. Our growth and culture haven't gone unnoticed - we've been recognized on Crain's Fast 50 and Inc. 5000 for remarkable growth, and named to Built In's 2025 Best Places to Work and Gallagher's 2022 Best-In-Class Employer for our workplace culture and benefits. Don't meet every single requirement? That's okay. We know growth comes from people who are willing to learn and challenge themselves, not just check every box. If you're energized and inspired by meaningful work, continuous development, and being part of a team that turns collective effort into real impact, we want to hear from you. We look forward to receiving your application. Our office locations are designed to bring people together, foster collaboration, and build meaningful connections. While some roles are based at one of our office locations, remote status and role locations are subject to change, and relocation is not provided.

Salary Context

This $152K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).

View full AI/ML Engineer salary data →

Role Details

Title Principal AI Operations Engineer (Remote)
Location Oak Brook, IL, US
Category AI/ML Engineer
Experience Senior
Salary $152K - $220K
Remote Yes

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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Inspira Financial, 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

Gcp (6% of roles) Python (20% of roles)

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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($186K) sits 12% below the category median. Disclosed range: $152K to $220K.

Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.

Inspira Financial AI Hiring

Inspira Financial has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in Oak Brook, IL, US. Compensation range: $150K - $220K.

Remote Work Context

Remote AI roles pay a median of $188,200 across 2,430 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.

The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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

Based on 13,780 roles with disclosed compensation, the median salary for AI/ML Engineer positions is $212,040. Actual compensation varies by seniority, location, and company stage.
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.
About 6% of the 11,520 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Inspira Financial is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI/ML Engineer positions include ML Architect, AI Engineering Manager, Principal ML Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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