Job description

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! Not the right fit? Let us know you re interested in a future opportunity by clicking Introduce Yourself in the top-right corner of the page or create an account to set up email alerts as new job postings become available that meet your interest!

Requirements

Must-haves

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.
MLOps Engineer - Consultant
Scope of Work
Model Serving - Synchronous and Asynchronous
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.

Nice-to-haves

Experience operationalizing GenAI or LLM-based applications and services.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmldockerkubernetesci/cdpythonawsllm

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Parental Leave