MLOps Engineer

MLOps Engineer – Consultant

Senior consulting role to operationalize machine learning solutions in purchase and underwriting. Hands‑on engineering role focused on writing production code—designing services, hardening pipelines, and ensuring ML production solutions are deployed, monitored, and consistent across batch and real‑time paths.

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, covering producers and consumers, worker concurrency, retries, 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 to enable reliable scaling, rollout, and rollback.

Own the data preprocessing, transformation, and feature engineering code between raw sources and the model, refactoring notebook or script‑style logic into modular, tested, and reusable components.

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 to ensure reproducibility.

Guarantee that the feature values a model sees at training time match those at batch scoring and real‑time serving through consistency in definitions, transformations, and edge‑case handling.

Establish parity checks and reconciliation between training data, batch outputs, and real‑time predictions as part of the pipeline.

Implement monitoring for model performance, prediction drift, data quality, and pipeline health with actionable alerts routed to the relevant owners.

Diagnose production incidents in pipelines and services, identify root causes, and drive fixes through to closure.

Apply strong software engineering fundamentals—testing, code review, CI/CD, semantic versioning, and dependency hygiene—to ML code.

Build and maintain shared libraries, utilities, and repository patterns usable by other ML use cases.

Create clear documentation of pipelines, frameworks, and operational runbooks to enable smooth ownership transfer.

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 seamlessly to internal teams.

• Deep hands‑on Python for data engineering and application development; comfortable with 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 using Kafka, RabbitMQ, SQS, Redis Streams, Celery, or equivalent, with operational concerns such as retries, idempotency, back‑pressure, and dead‑letter queues.
• Strong Docker and general containerization skills; comfortable with Kubernetes concepts.
• Hands‑on Databricks experience including MLFlow and distributed compute in Spark.
• Working experience with common ML libraries (scikit‑learn, XGBoost, PyTorch, or similar) sufficient to partner with data scientists.
• Strong grasp of the end‑to‑end ML lifecycle and experience building or migrating feature engineering code with training/batch/realtime parity focus.
• Comfort reading and refactoring batch ML or data pipeline code while preserving intent and edge cases.
• Experience with CI/CD tools (Jenkins, Git Hub Actions, or equivalent), version control workflows, and orchestration (Airflow, Prefect, or equivalent).
• Excellent written and verbal communication skills; able to drive alignment without middle manager…