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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.
Please visit Fractal \| Intelligence for Imagination for more information about Fractal.
MLOps Engineer — Consultant
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The Engagement
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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 with our AI / ML Ops team and partner with our Data Science and Data Engineering teams to deliver the inference layer, data and feature pipelines.
This is a hands\-on engineering role. You should expect to spend most of your time writing production code — designing services, hardening pipelines, and to get ML production solutions developed, deployed, monitored, and consistent across batch and real\-time paths.
Scope of Work
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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 semantics, and structured logging, tracing, and metrics.
- Implement queue\-based asynchronous serving for higher\-latency or higher\-throughput workloads — producers and consumers, worker concurrency, retries and back\-off, dead\-letter handling, back\-pressure, idempotency, and end\-to\-end traceability of a request across the pipeline.
- Containerize services with Docker and deploy them so that scaling, rollout, and rollback are boring.
Data preprocessing, feature engineering, and pipelines
- Own the data preprocessing, transformation, and feature engineering code that sits between raw sources and the model — refactoring notebook or script\-style logic into modular, tested, and reusable components.
- Work with existing code from prior batch solutions: read it carefully, understand the business logic and edge cases baked in, and evolve it into the target\-state pipelines rather than throwing it away.
- Build reproducible training and batch inference pipelines on Databricks and PySpark, from raw sources through curated feature and training datasets.
- Manage model artifacts, versions, and promotion across environments so that what runs in production is always known and reproducible.
Data and feature parity across the ML lifecycle
- Guarantee that the feature values a model sees at training time match what it sees at batch scoring and real\-time serving — same definitions, same transformations, same edge\-case handling.
- Design feature engineering code so that a single implementation (or a rigorously validated pair) serves both offline (Spark/batch) and online (low\-latency Python) paths, avoiding the classic “training/serving skew” failure mode.
- Establish parity checks and reconciliation between training data, batch outputs, and real\-time predictions as a first\-class part of the pipeline — not an afterthought.
- Bring a solid working understanding of the end\-to\-end ML lifecycle — from data acquisition, preprocessing, and feature engineering through training, evaluation, deployment, monitoring, and retraining — and use that lens to make design trade\-offs across batch and real\-time solutions.
Reliability and observability
- Implement monitoring for model performance, prediction drift, data quality, and pipeline health, with actionable alerts routed to the right owners.
- Diagnose production incidents in pipelines and services, identify root causes, and drive fixes through to closure — including the durable fix, not just the mitigation.
Engineering practices
- Apply strong software engineering fundamentals — testing, code review, CI/CD, semantic versioning, and dependency hygiene — to ML code that has historically not had them.
- Build and maintain shared libraries, utilities, and repository patterns that other ML use cases can adopt.
- Document what you build clearly enough that internal teams can own it after the engagement ends.
Collaborate and document
- Work closely with Data Science, Data Engineering, business partners, and IT teams to align on requirements, handoffs, and production readiness.
- Produce clear documentation of pipelines, frameworks, and operational runbooks so ownership can transition smoothly to internal teams.
What You Bring
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Required
- Deep hands\-on Python in using it for both data engineering and application development, and comfort across SQL, PySpark, and shell scripting.
- Production experience building services with FastAPI (or a comparable Python web framework), including auth, validation, error handling, and observability.
- Experience building queue\-based asynchronous processing systems — familiarity with at least one of Kafka, RabbitMQ, SQS, Redis Streams, Celery, or equivalent — and the operational concerns that come with them (retries, idempotency, back\-pressure, dead\-letter queues).
- Strong Docker and general containerization skills; comfortable with Kubernetes concepts even if a platform team runs the cluster.
- Hands\-on Databricks experience including working knowledge of MLFlow and fluency with distributed compute in Spark.
- Working experience with common ML libraries (scikit\-learn, XGBoost, PyTorch or similar) — enough to be a competent partner to data scientists, not necessarily to build novel models.
- Strong grasp of the end\-to\-end ML lifecycle and a track record of building or migrating feature engineering code with an explicit focus on training / batch / real\-time parity.
- Comfort reading and refactoring batch ML or data pipeline code — understanding intent and edge cases before rewriting.
- CI/CD (Jenkins, GitHub Actions, or equivalent), version control workflows, and orchestration (Airflow, Prefect, or equivalent).
- Excellent written and verbal communication; able to drive alignment with data scientists, platform engineers, and business stakeholders without a manager brokering every conversation.
Nice to Have
- Prior Experience working in Group Insurance Domain or Life Insurance Underwriting Domain.
- Experience operationalizing LLM\-based systems — inference serving, evaluation, cost and latency controls.
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: $120,000 to $140,000 Yearly. 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 plan 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!
### Hiring Related Queries
India: HiringsupportIndia@fractal.ai
Outside India: HiringsupportROW@fractal.ai
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Salary Context
This $120K-$140K range is in the lower quartile 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 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 $220,000 based on 47 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($130K) sits 41% below the category median. Disclosed range: $120K to $140K.
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.
Fractal Analytics AI Hiring
Fractal Analytics has 4 open AI roles right now. They're hiring across Data Scientist, MLOps Engineer, AI/ML Engineer. Positions span CA, US, New York, NY, US. Compensation range: $140K - $205K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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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