MLOps Engineer

About this role

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.   Please visit Fractal | Intelligence for Imagination for more information about Fractal.

MLOps Engineer — Consultant The Engagement 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. 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. 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 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.

Data preprocessing, feature engineering, and pipelines • 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.

Data and feature parity across the ML lifecycle • 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.

Reliability and observability • 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.

Engineering practices • 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.

Collaborate and document • 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.

What You Bring Required • 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

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