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About This Role
FICO (NYSE: FICO) is a leading global analytics software company, helping businesses in 100\+ countries make better decisions. Join our world\-class team today and fulfill your career potential!
The Opportunity
As a Lead MLOps/DevOps Engineer on our Generative AI team, you’ll be at the cutting edge of language model applications, building innovative solutions across key areas of the FICO platform—including fraud detection, decision automation, workflow orchestration, and system optimization and also internal productivity. You will deploy production systems, troubleshoot operational issues, and integrate services at scale. You’ll have the opportunity to make a measurable impact by bringing next\-generation AI capabilities into production, collaborating with a world\-class team to build robust, scalable infrastructure and accelerate innovation across FICO’s platform.
WhatYou’llContribute
- Design, build, and maintain scalable, resilient data and ML pipelines, infrastructure, and workflows using tools such as Terraform, GitHub Actions, ArgoCD, Helm, and others.
- Automate infrastructure provisioning and configuration management using cloud\-native services (preferably AWS) with tools like Terraform, CloudFormation.
- Design, containerize, and manage Kubernetes (EKS) clusters and/or ECS environments in AWS. Collaborate with development teams to optimize performance, deployment, and cost.
- Partner with DevOps and SRE teams to ensure high availability, observability, scalability, and security of the data and ML infrastructure.
- Work closely with Data Scientists and ML Engineers to operationalize machine learning models, including building CI/CD pipelines for model training, validation, and deployment.
- Implement observability for data pipelines and ML services using tools like Prometheus, Grafana, Datadog, or similar.
- Develop and maintain automated pipelines for model retraining, monitoring drift, and versioning in production.
- Support experimentation and prototyping in areas such as Machine Learning and Generative AI, transitioning successful prototypes into production systems.
- Ensure cloud infrastructure is secure, compliant, and cost\-efficient, following best practices in governance, identity, and access management.
WhatWe’reSeeking
- 8\+ years of experience in DataOps, MLOps, or related fields, with 3\+ years focused on ML model operationalization and workflow automation.
- Proficient in AWS services including EC2, S3, IAM, ACM, Route 53, CloudWatch, EKS, and ECS.
- Experience with infrastructure as code (IaC) tools such as Terraform, CloudFormation, and Helm.
- Familiarity with CI/CD for ML pipelines, GitOps practices, and tools like GitHub Actions, Jenkins, or Argo Workflows.
- Strong scripting and automation skills using Python, or GitHub workflows.
- Solid understanding of observability and monitoring tools (e.g., Prometheus, Grafana, Datadog, or OpenTelemetry).
- Solid understanding of security best practices for cloud and Kubernetes environments, including secrets management, identity \& access control, and policy enforcement.
- Strong understanding with data governance, lineage, and metadata management is a plus.
- Excellent collaboration and communication skills, with a proven ability to work effectively in cross\-functional, globally distributed teams.
- A bachelor’s degree in computer sciences, or a related discipline, or equivalent hands\-on industry experience.
Our Offer to You
- An inclusive culture strongly reflecting our core values: Act Like an Owner, Delight Our Customers and Earn the Respect of Others.
- The opportunity to make an impact and develop professionally by leveraging your unique strengths and participating in valuable learning experiences.
- Highly competitive compensation, benefits and rewards programs that encourage you to bring your best every day and be recognized for doing so.
- An engaging, people\-first work environment offering work/life balance, employee resource groups, and social events to promote interaction and camaraderie.
- The targeted base pay range for this role is: $140,000 to $220,000 with this range reflecting differences in candidate knowledge, skills and experience.
\#LI\-LS1
\#LI\-Remote
Why Make a Move to FICO?
At FICO, you can develop your career with a leading organization in one of the fastest\-growing fields in technology today – Big Data analytics. You’ll play a part in our commitment to help businesses use data to improve every choice they make, using advances in artificial intelligence, machine learning, optimization, and much more.
FICO makes a real difference in the way businesses operate worldwide:
- Credit Scoring — FICO® Scores are used by 90 of the top 100 US lenders.
- Fraud Detection and Security — 4 billion payment cards globally are protected by FICO fraud systems.
- Lending — 3/4 of US mortgages are approved using the FICO Score.
Global trends toward digital transformation have created tremendous demand for FICO’s solutions, placing us among the world’s top 100 software companies by revenue. We help many of the world’s largest banks, insurers, retailers, telecommunications providers and other firms reach a new level of success. Our success is dependent on really talented people – just like you – who thrive on the collaboration and innovation that’s nurtured by a diverse and inclusive environment. We’ll provide the support you need, while ensuring you have the freedom to develop your skills and grow your career. Join FICO and help change the way business thinks!
Learn more about how you can fulfil your potential at www.fico.com/Careers
FICO promotes a culture of inclusion and seeks to attract a diverse set of candidates for each job opportunity. We are an equal employment opportunity employer and we’re proud to offer employment and advancement opportunities to all candidates without regard to race, color, ancestry, religion, sex, national origin, pregnancy, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Research has shown that women and candidates from underrepresented communities may not apply for an opportunity if they don’t meet all stated qualifications. While our qualifications are clearly related to role success, each candidate’s profile is unique and strengths in certain skill and/or experience areas can be equally effective. If you believe you have many, but not necessarily all, of the stated qualifications we encourage you to apply.
Information submitted with your application is subject to the FICO Privacy policy at https://www.fico.com/en/privacy\-policy
Salary Context
This $140K-$220K range is above the median for MLOps Engineer roles in our dataset (median: $177K across 20 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 3,708 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At FICO, 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 $220,000 based on 47 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($180K) sits 18% below the category median. Disclosed range: $140K to $220K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
FICO AI Hiring
FICO has 2 open AI roles right now. They're hiring across MLOps Engineer, AI Software Engineer. Based in Remote, US. Compensation range: $220K - $269K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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