URGENT
ML contractor 

Term: 3 months
Openings: 1
Location: Remote (U.S. time zones; must overlap ?4 hours with U.S. Central Time)
Start: ASAP

Overview
We are seeking a senior Python ML engineer to lead the migration of multiple analytics and machine learning applications from a legacy SQL environment to Amazon Redshift. In addition, the codebases need to be standardized on a modern Python architecture that supports best practices for deployment, testing, and maintainability. This role combines hands-on work with mentoring, ensuring sustainable practices across the team.

Key Responsibilities

• Review existing Python applications to map dependencies, data access patterns, configuration, and deployment processes.

• Transition data pipelines to pull from Redshift while eliminating legacy SQL dependencies.

• Standardize code organization, packaging, configuration, logging, and containerization according to a modern reference framework.

• Develop unit and integration tests for data ingestion, transformations, and model outputs, integrating them into CI/CD pipelines.

• Document code, add clear type hints, improve readability, and produce operational runbooks for all applications.

• Update deployment pipelines using containerization and orchestration tools to ensure repeatable, automated releases.

• Provide guidance and training to engineers on modern development standards, testing practices, and Redshift integration.

Expected Deliverables

• Week 1: Conduct application inventory, define architecture targets, and begin updating the first application (data layer, tests, documentation).

• Week 2: Complete first app migration, validate in a staging environment, and begin work on a second application.

• Week 3+: Continue migrating ~2 applications per week, including code standardization, testing, documentation, and deployment automation, until all applications are fully transitioned.

Required Skills and Experience

• 7+ years of professional experience developing production ML or analytics applications in Python.

• Strong knowledge of Python project structures, dependency management, and packaging tools (pip, poetry, conda).

• Experience migrating applications from legacy SQL databases to cloud data warehouses (Redshift, Snowflake, BigQuery), ensuring data consistency.

• Proficiency in SQL and experience optimizing queries for cloud warehouses.

• Demonstrated ability to write robust tests (pytest/unittest) and integrate them with CI/CD pipelines.

• Familiarity with containerization, orchestration, and workflow tools such as Docker, Kubernetes, Airflow, or Step Functions.

• Strong documentation skills and ability to coach other engineers on sustainable development practices.

Preferred Skills

• Experience with dbt-modeled data warehouses and collaboration with analytics engineers.

• Knowledge of MLOps tools, model validation frameworks, and feature stores.

• Ability to implement automated testing frameworks and data quality checks for ML pipelines.

Success Metrics

• All Python ML and analytics applications migrated to Redshift with verified parity.

• Applications updated to a modern architecture, complete with testing, documentation, and deployment automation.

• Team empowered with guidance, processes, and runbooks to maintain the applications independently after the engagement.