MLOps Engineer

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.

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.

• 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…