MLOps Engineer

Fractal

. Operationalize a portfolio of machine learning solutions in purchase and underwriting .

Posted 10/7/2026full-timeUnited StatesMid-LevelSenior💰 $100,000 - $125,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates deep expertise in Python for data engineering and backend application development, with a strong focus on building and operationalizing machine learning solutions using FastAPI, Databricks, and MLOps practices. Proficient in designing scalable services and implementing CI/CD pipelines while ensuring effective collaboration with technical and business stakeholders.

Highest-signal resume keywords

Python ExpertiseFastAPI DevelopmentDatabricks and MLflowAWS Services (SQS, EKS)Docker and Kubernetes

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine Learning SolutionsProduction Code DevelopmentModel Inference PatternsData PreprocessingFeature EngineeringCI/CD ImplementationEvent-Driven ArchitecturesMonitoring and DiagnosticsArchitecture DesignVersion Control

Soft Skills

Excellent Communication SkillsCollaboration with Stakeholders

Tools & Technologies

FastAPIDatabricksMLflowDelta LakeDockerKubernetesGitHub ActionsJenkinsSQSKafka

Industry Keywords

Group InsuranceLife InsuranceUnderwritingMLOpsGenAILLM-based Applications

Tech Stack

Tools & technologies

AWSDockerJenkinsKafkaKubernetesPostgresPySparkPythonSpark

About the role

Key responsibilities & impact

Operationalize a portfolio of machine learning solutions in purchase and underwriting
Write production code and design scalable services across development, test, and production environments
Design and build FastAPI services for synchronous and asynchronous model serving
Implement queue-based serving patterns using SQS and Kafka
Containerize services with Docker and deploy them to Kubernetes/EKS
Design batch and real-time model inference patterns
Implement MLflow-based model training, experiment tracking, registry, deployment, and inference workflows
Build reproducible ML workflows and manage model artifacts, versions, lineage, and deployment governance
Establish standardized MLOps frameworks and reusable implementation patterns
Build Databricks and PySpark training and inference pipelines
Develop pipelines using MLflow, Delta Lake, Databricks Workflows, and Databricks Asset Bundles
Own data preprocessing, transformation, and feature engineering code
Participate in solution design with Data Science, Data Engineering, Platform Engineering, business, and IT stakeholders
Create architecture diagrams, solution blueprints, design documents, and implementation runbooks
Evaluate scalability, maintainability, performance, and operational complexity tradeoffs
Provide technical guidance on MLOps practices, deployment approaches, and platform adoption
Implement monitoring for model performance, prediction drift, data quality, service health, and pipeline reliability
Diagnose production incidents, identify root causes, and drive durable fixes
Establish deployment, monitoring, alerting, and supportability standards
Apply testing, code reviews, CI/CD, versioning, dependency management, and infrastructure automation
Build and maintain shared libraries, starter templates, reusable frameworks, and engineering standards
Use GitHub Copilot and Claude Code to improve productivity, code quality, and delivery velocity
Document solutions for internal teams to operate after the engagement concludes

Requirements

What you’ll need

Deep hands-on Python expertise for data engineering and backend application development
Strong experience developing production-grade backend services using FastAPI
Hands-on expertise with Databricks, including MLflow, Delta Lake, Databricks Workflows, Databricks Asset Bundles (DABs), and 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 in day-to-day software engineering workflows
Excellent written and verbal communication skills for collaboration with technical and business stakeholders
Prior experience in Group Insurance, Life Insurance, or Underwriting domains is nice to have
Experience operationalizing GenAI or LLM-based applications and services is nice to have

Benefits

Comp & perks

Discretionary bonus eligibility
Health insurance
Dental insurance
Vision insurance
Life insurance
Disability plans
401(k) Plan after 30 days of employment
11 paid holidays
12 weeks of Parental Leave
“Free time” PTO policy for sick time or vacation
Benefits eligibility on the first day of employment