MLOps Engineer, Fractal

• This is a hands-on engineering role. You should expect to spend most of your time writing production code — designing services, hardening pipelines, and to get ML production solutions developed, deployed, monitored, and consistent across batch and real-time paths
• Deep hands-on Python in using it for both data engineering and application development, and comfort across SQL, PySpark, and shell scripting
• Production experience building services with FastAPI (or a comparable Python web framework), including auth, validation, error handling, and observability
• Experience building queue-based asynchronous processing systems — familiarity with at least one of Kafka, RabbitMQ, SQS, Redis Streams, Celery, or equivalent — and the operational concerns that come with them (retries, idempotency, back-pressure, dead-letter queues)
• Strong Docker and general containerization skills; comfortable with Kubernetes concepts even if a platform team runs the cluster
• Hands-on Databricks experience including working knowledge of MLFlow and fluency with distributed compute in Spark
• Working experience with common ML libraries (scikit-learn, XGBoost, PyTorch or similar) — enough to be a competent partner to data scientists, not necessarily to build novel models
• Strong grasp of the end-to-end ML lifecycle and a track record of building or migrating feature engineering code with an explicit focus on training / batch / real-time parity
• Comfort reading and refactoring batch ML or data pipeline code — understanding intent and edge cases before rewriting
• CI/CD (Jenkins, GitHub Actions, or equivalent), version control workflows, and orchestration (Airflow, Prefect, or equivalent)
• Excellent written and verbal communication; able to drive alignment with data scientists, platform engineers, and business stakeholders without a manager brokering every conversation

Desirable

• 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 with our AI / ML Ops team and partner with our Data Science and Data Engineering teams to deliver the inference layer, data and feature pipelines
• Design and build FastAPI services that expose models to downstream applications, including request/response contracts, authentication and authorization, input validation, error semantics, and structured logging, tracing, and metrics
• Implement queue-based asynchronous serving for higher-latency or higher-throughput workloads — producers and consumers, worker concurrency, retries and back-off, dead-letter handling, back-pressure, idempotency, and end-to-end traceability of a request across the pipeline
• Containerize services with Docker and deploy them so that scaling, rollout, and rollback are boring
• Own the data preprocessing, transformation, and feature engineering code that sits between raw sources and the model — refactoring notebook or script-style logic into modular, tested, and reusable components
• Work with existing code from prior batch solutions: read it carefully, understand the business logic and edge cases baked in, and evolve it into the target-state pipelines rather than throwing it away
• Build reproducible training and batch inference pipelines on Databricks and PySpark, from raw sources through curated feature and training datasets
• Manage model artifacts, versions, and promotion across environments so that what runs in production is always known and reproducible
• Guarantee that the feature values a model sees at training time match what it sees at batch scoring and real-time serving — same definitions, same transformations, same edge-case handling
• Design feature engineering code so that a single implementation (or a rigorously validated pair) serves both offline (Spark/batch) and online (low-latency Python) paths, avoiding the classic “training/serving skew” failure mode
• Establish parity checks and reconciliation between training data, batch outputs, and real-time predictions as a first-class part of the pipeline — not an afterthought
• Bring a solid working understanding of the end-to-end ML lifecycle — from data acquisition, preprocessing, and feature engineering through training, evaluation, deployment, monitoring, and retraining — and use that lens to make design trade-offs across batch and real-time solutions
• Implement monitoring for model performance, prediction drift, data quality, and pipeline health, with actionable alerts routed to the right owners
• Diagnose production incidents in pipelines and services, identify root causes, and drive fixes through to closure — including the durable fix, not just the mitigation
• Apply strong software engineering fundamentals — testing, code review, CI/CD, semantic versioning, and dependency hygiene — to ML code that has historically not had them
• Build and maintain shared libraries, utilities, and repository patterns that other ML use cases can adopt
• Document what you build clearly enough that internal teams can own it after the engagement ends
• Work closely with Data Science, Data Engineering, business partners, and IT teams to align on requirements, handoffs, and production readiness
• Produce clear documentation of pipelines, frameworks, and operational runbooks so ownership can transition smoothly to internal teams

• End Date: August 17, 2026