MLOps Engineer

Fractal Analytics•New York, NY

1d•$100,000 - $125,000

About The Position

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.

Requirements

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 Haves

Prior experience working in Group Insurance, Life Insurance, or Underwriting domains.
Experience operationalizing GenAI or LLM-based applications and services.

Responsibilities

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.
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.
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.
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.
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.
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.