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About This Role
It's fun to work in a company where people truly BELIEVE in what they are doing!
We're committed to bringing passion and customer focus to the business.
Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.
Role Summary
MLOps Engineer - Consultant
We are hiring a senior MLOps Engineer on a consulting basis to help operationalize a portfolio of machine learning solutions in purchase and underwriting. You will work alongside our AI/MLOps team and partner with our Data Science and Data Engineering teams to deliver inference services, data pipelines, feature engineering frameworks, and model lifecycle management capabilities.
This is a hands-on engineering role. You should expect to spend most of your time writing production code, designing scalable services, building reusable ML platform capabilities, and ensuring machine learning solutions are production-ready across development, test, and production environments.
Scope of Work
Model Serving - Synchronous and Asynchronous
- Design and build FastAPI services that expose models to downstream applications, including request/response contracts, authentication and authorization, input validation, error handling, structured logging, tracing, and metrics.
- Implement queue-based asynchronous serving patterns for higher-latency and higher-throughput workloads using technologies such as SQS and Kafka.
- Containerize services with Docker and deploy them onto Kubernetes/EKS environments with production-grade observability and scalability.
- Design and implement model inference patterns across both batch and real-time serving use cases.
ML Platform and Lifecycle Management
- Design and implement end-to-end MLflow-based patterns for model training, experiment tracking, model registry, deployment, and inference.
- Build and manage reproducible ML workflows, ensuring seamless model promotion across development, testing, and production environments.
- Manage model artifacts, versions, lineage, and deployment governance using MLflow and Databricks-native capabilities.
- Establish standardized MLOps frameworks and reusable implementation patterns that can be adopted across multiple ML use cases.
Data Engineering and Feature Pipelines
- Build reproducible training and inference pipelines on Databricks and PySpark, from raw sources through curated feature and training datasets.
- Design and develop pipelines leveraging Databricks capabilities including MLflow, Delta Lake, Databricks Workflows, and Databricks Asset Bundles (DABs).
- Own data preprocessing, transformation, and feature engineering code and evolve existing solutions into scalable, reusable production frameworks.
- Ensure consistent feature engineering and data processing logic across training, batch inference, and real-time serving paths.
Solution Architecture and Design
- Participate in solution design discussions with Data Science, Data Engineering, Platform Engineering, business, and IT stakeholders.
- Create technical architecture diagrams, solution blueprints, design documents, and implementation runbooks.
- Evaluate tradeoffs between scalability, maintainability, performance, and operational complexity while defining target-state architectures.
- Provide technical guidance and recommendations on MLOps best practices, deployment approaches, and platform adoption.
Reliability and Observability
- Implement monitoring for model performance, prediction drift, data quality, service health, and pipeline reliability.
- Diagnose production incidents across pipelines and services, identify root causes, and drive durable fixes.
- Establish operational standards for deployment, monitoring, alerting, and supportability.
Engineering Practices
- Apply strong software engineering fundamentals including testing, code reviews, CI/CD, versioning, dependency management, and infrastructure automation.
- Build and maintain shared libraries, starter templates, reusable frameworks, and engineering standards.
- Leverage GitHub Copilot and Claude Code to improve developer productivity, code quality, and delivery velocity.
- Document solutions clearly enough for internal teams to own and operate after the engagement concludes.
What You Bring
Required
- Deep hands-on Python expertise for both data engineering and backend application development.
- Strong experience developing production-grade backend services using FastAPI.
- Hands-on expertise with the Databricks platform, including:
- MLflow
- Delta Lake
- Databricks Workflows
- Databricks Asset Bundles (DABs)
- Spark-based distributed processing
- Proven experience designing and implementing end-to-end MLflow-based training and inference architectures across multiple environments.
- Experience with AWS services including SQS, EKS, and Aurora PostgreSQL.
- Experience implementing event-driven and asynchronous architectures using Kafka and/or SQS.
- Strong understanding of the end-to-end ML lifecycle, including feature engineering, training, deployment, monitoring, and retraining.
- Experience creating architecture diagrams, technical design documentation, implementation plans, and operational runbooks.
- Strong Docker and Kubernetes fundamentals.
- Experience with CI/CD pipelines using GitHub Actions, Jenkins, or similar tools.
- Hands-on experience using both GitHub Copilot and Claude Code as part of day-to-day software engineering workflows.
- Excellent written and verbal communication skills with the ability to collaborate effectively with technical and business stakeholders.
Nice to Have:
- Prior experience working in Group Insurance, Life Insurance, or Underwriting domains.
- Experience operationalizing GenAI or LLM-based applications and services.
Pay:
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: $100,000 - $125,000. In addition, you may be eligible for a discretionary bonus for the current performance period.
Benefits:
As a full-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plans in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of Employment with the Company. In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides 11 paid holidays and 12 weeks of Parental Leave. We also follow a “free time” PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation.
Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
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Salary Context
This $100K-$125K range is in the lower quartile for MLOps Engineer roles in our dataset (median: $172K across 12 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 9,079 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Fractal Analytics, 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 $204,000 based on 162 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($112K) sits 45% below the category median. Disclosed range: $100K to $125K.
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.
Fractal Analytics AI Hiring
Fractal Analytics has 2 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Positions span NJ, US, New York, NY, US. Compensation range: $125K - $130K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national 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 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).
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 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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