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About This Role
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*By joining the American Red Cross you will touch millions of lives every year and experience the greatness of the human spirit at its best. Are you ready to be part of the world's largest humanitarian network?*
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Join us—Where your Career is a Force for Good!
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Job Description:
WHY CHOOSE US?
Joining The American Red Cross is like nothing else – it’s as much something you feel as something you do. You become a vital part of the world’s largest humanitarian network. Joining a team of welcoming individuals who are exceptional, yet unassuming. Diverse, yet uncompromising in unity. You grow your career within a movement that matters, where success is measured in people helped, communities made whole, and individuals equipped to never stop changing lives and situations for the better.
When you choose to be a force for good, you’ll have mentors who empower your growth along a purposeful career path. You align your life’s work with an ongoing mission that’s bigger than all of us. As you care for others, you’re cared for with competitive compensation and benefits. You join a community that respects who you are away from work as much as what you do while at work.
WHAT YOU NEED TO KNOW ABOUT THE JOB:
The Enterprise AI team at the American Red Cross is hiring an AI/ML Platform Operations Analyst to operate our Dataiku and Databricks environments. You’ll be a member of a small, senior team supporting roughly 10 data scientists and a steady cadence of production analytics and ML/AI work in service of humanitarian, biomed, and training services operations.
The role sits at the data\-science workflow layer: partnering with data scientists and developers to move completed projects into production, maintaining operational health of pipelines across both platforms, and providing operational counterweight to a team that otherwise leans toward platform engineering.
The work location for this exciting opportunity is virtual.
The selected candidate will work 100% remotely from home and can be located anywhere in the United States but must work an east coast schedule.
WHERE YOUR CAREER IS A FORCE FOR GOOD (Key Responsibilities):
- Partner with data scientists and developers to move completed projects from development to production across the Dataiku and Databricks platforms—packaging code environments (uv, venv, Dataiku code envs), establishing monitoring and alerting, and watching for performance degradation.
- Triage and resolve pipeline failures across both platforms; coordinate with upstream data owners (Redshift, Postgres, S3\) when source systems, credentials, or schemas change.
- Maintain visibility into the project intake funnel: what is coming, what is blocked, what is promotable. Surface upcoming operational needs proactively.
- Author and maintain operational runbooks and documentation.
- Perform user, group, code environment, and cluster policy administration within the Dataiku and Databricks consoles.
- Partner with the Principal Engineer on DataOps automation requirements, design feedback, and testing.
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Scope: Individual contributor with comprehensive knowledge in specific area. Ability to execute highly complex or specialized projects. Adapt precedent and may make significant departures from traditional approaches to develop solutions.
Note: Qualified candidates must be authorized to work in the United States. The American Red Cross does not sponsor employment visas.
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WHAT YOU NEED TO SUCCEED (required/minimum qualifications):
- Education: Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field—or equivalent professional experience.
- Experience: 7 years in MLOps, DataOps, data engineering, or related platform operations roles.
- Significant production experience with both Dataiku and Databricks. Familiarity with one platform alone is not sufficient for this seat.
- Hands\-on experience with the AWS data stack (S3, IAM, Redshift) and strong proficiency in Python and modern environment management (uv, pip, venv, or equivalent).
- Demonstrated experience supporting data scientists and analysts through the development\-to\-production lifecycle.
- Strong operational judgment—ability to determine when to automate, when to runbook, and when to leave a working manual process alone.
- Excellent written and verbal communication skills, with demonstrated diplomacy across data science, data engineering, security, and infrastructure stakeholders.
WHAT WILL GIVE YOU THE COMPETITIVE EDGE (Preferred Qualifications):
- Familiarity with infrastructure\-as\-code (Terraform) and Git\-based workflows.
- Experience with model monitoring, data quality tooling, or AI/ML observability platforms.
- Experience operating in compliance\-sensitive environments (healthcare, financial services, government, or humanitarian operations).
- Prior experience in nonprofit, humanitarian, or mission\-driven organizations.
- Ability to clearly articulate hands\-on involvement in prior work.
- Combination of candidate’s education and general experience satisfies requirements so long as the total years equate to description’s minimum education and general experience years combined (Certification cannot be substituted).
Physical Requirements
Physical requirements are those present in normal office environment conditions. Operational flexibility is required to meet sudden and unpredictable needs. Ability to use a personal computer, applicable software, and office equipment for sustained periods of time. May include sitting for long periods of time, driving a vehicle, and working under challenging conditions.
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PAY INFORMATION:
The annual salary range for this position is $110K \- $120K. We do not offer an annual bonus for this role.
Note that American Red Cross salaries are aligned to the specific geographic location in which the work is primarily performed. Other factors that may be used to determine your actual salary may include your specific skills, how many years of experience you have and comparison to other employees already in this role. \*\*We will review specific salary information at the time of phone screening based upon your location \& experience.\*\*
This job will be posted for a minimum of five business days and extended if the applicant pool needs to be expanded.
BENEFITS FOR YOU:
- As a mission\-based organization, we believe our team needs great support to do great work. Our comprehensive package includes:
- Medical, Dental Vision plans
- Health Spending Accounts \& Flexible Spending Accounts
- PTO: Starting at 19 days a year; based on type of job and tenure
- Holidays: 11 paid holidays comprised of six core holidays and five floating holidays
- 401K with up to 6% match
- Paid Family Leave
- Employee Assistance
- Disability and Insurance: Short \+ Long Term
- Service Awards and recognition
- LI\-EH1
*Apply now! Joining our team will provide you with the opportunity to*
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*make your career a force for good!*
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*The American Red Cross is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected veteran status, age, or any other characteristic protected by law.*
*Qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers, San Diego Fair Chance Ordinance, the California Fair Chance Act and any other applicable state and local laws.*
AmeriCorps, the federal agency that brings people together through service, and its partners — the Peace Corps, AmeriCorps Alums, National Peace Corps Association, and the Service Year Alliance — launched Employers of National Service to connect national service alumni with opportunities in the workforce. American Red Cross is proud to be an EONS partner and share our employment opportunities with the network of organizations.
Interested in Volunteering? Visit redcross.org/volunteertoday to learn more, including our most\-needed volunteer positions.
To view the EEOC Summary of Rights, click here: Summary of Rights
Salary Context
This $110K-$120K range is in the lower quartile for MLOps Engineer roles in our dataset (median: $168K across 34 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 4,317 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At American Red Cross, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $203,000 based on 85 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($115K) sits 43% below the category median. Disclosed range: $110K to $120K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
American Red Cross AI Hiring
American Red Cross has 1 open AI role right now. They're hiring across MLOps Engineer. Based in NC, US. Compensation range: $120K - $120K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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
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