MLOps Engineer

Fractal; New York (NY); On-site; USD 100,000 - 125,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Life insurance

Disability plans

401(k) after 30 days

11 paid holidays

12 weeks of Parental Leave

Free time PTO

Job summary

Fractal seeks a senior MLOps Engineer on a consulting basis to operationalize a portfolio of ML solutions in purchase and underwriting. You will collaborate with our AI/MLOps team and partner with Data Science and Data Engineering to deliver inference services, data pipelines, feature frameworks, and model lifecycle management.

Focus areas include building production-grade backend services with FastAPI, Databricks platforms (MLflow, Delta Lake, Workflows, DABs), and containerized deployments on

Qualifications

• Deep hands-on Python experience for data engineering and backend development.
• Strong background building production-grade backend services with FastAPI.
• Hands-on expertise with Databricks: MLflow, Delta Lake, Workflows, DABs.
• Spark-based distributed processing experience.
• Proven end-to-end MLflow training and inference architectures across environments.
• Experience with AWS services including SQS, EKS, and Aurora PostgreSQL.
• Event-driven and asynchronous architectures using Kafka and/or SQS.
• Solid understanding of the full ML lifecycle: feature engineering, training, deployment, monitoring, retraining.
• Ability to craft architecture diagrams, design docs, runbooks, and deployment plans.
• Strong Docker and Kubernetes fundamentals; familiarity with CI/CD (GitHub Actions, Jenkins).
• Hands-on use of GitHub Copilot and Claude Code in daily workflows.
• Excellent written and verbal communication for collaboration with stakeholders.

Responsibilities

• Design and build FastAPI services exposing models with contracts, auth, input validation, and observability.
• Implement asynchronous serving patterns with SQS/Kafka for higher throughput.
• Containerize services with Docker and deploy to Kubernetes/EKS with production-grade observability.
• Define model inference patterns for batch and real-time use cases.
• Create end-to-end MLflow-based training, tracking, registry, deployment, and inference workflows.
• Manage reproducible ML workflows and production-ready data pipelines on Databricks.

Skills

Python

FastAPI

Docker & Kubernetes

AWS

Kafka

MLflow

Databricks

Spark

CI/CD

GitHub Copilot

Claude Code

Tools

Databricks

MLflow

Delta Lake

Databricks Workflows

Databricks Asset Bundles (DABs)

Spark

Kubernetes

Docker

SQS

Kafka

Jenkins

GitHub Actions

Job description

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. 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.

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.

Required

What You Bring

• 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.