Position Summary

We are seeking a skilled Machine Learning Engineer with approximately three years of hands-on experience designing, deploying, and maintaining production-grade machine learning systems. In this role, you will collaborate closely with data scientists, software engineers, and product teams to translate research models into reliable, scalable, and high-impact applications. You will be deeply involved in the end-to-end ML lifecycle—from data ingestion and feature engineering to deployment, monitoring, and continuous improvement—playing a critical part in shaping our machine learning platform and capabilities.

Primary Responsibilities

• Develop, deploy, and optimize machine learning models for real-world business use cases and client-facing applications.
• Partner with data scientists to operationalize predictive models and ensure scalable, maintainable, and performant production deployments.
• Design and implement data pipelines and workflows that support training, inference, and model lifecycle management.
• Work with large, complex datasets to ensure data quality, reproducibility, and reliable version control across ML workflows.
• Implement model monitoring, logging, and alerting strategies to track performance, detect drift, and support retraining cycles.
• Leverage cloud platforms (AWS, Azure, GCP) to build scalable ML solutions using managed services and infrastructure-as-code practices.
• Write clean, modular, and well-documented code aligned with MLOps and software engineering best practices.
• Stay current on emerging ML tooling, frameworks, and industry best practices to continuously enhance our platform and capabilities.

Qualifications

• Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
• 6+ years of experience in machine learning engineering, applied ML, or related software engineering roles.
• Strong proficiency in Python and experience with modern ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience with distributed data processing and compute frameworks (e.g., Pandas, Spark, Dask).
• Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Familiarity with CI/CD pipelines, testing automation, and version control using Git.
• Experience working with cloud-based ML platforms or services (e.g., SageMaker, Vertex AI, Databricks, or Snowflake ML) is preferred.
• Strong understanding of model evaluation, feature engineering, and performance optimization in production contexts.
• Excellent analytical, communication, and collaboration skills, with the ability to work effectively in cross-functional teams.

This is an exempt position. The annualized base pay range for this role is expected to be between $140,000-$180,000.  Actual base pay could vary based on factors including but not limited to experience, subject matter expertise, geographic location where work will be performed, and the applicant's skill set.  The base pay is just one component of the total compensation package for employees.  Other rewards may include an annual cash bonus and a comprehensive benefits package, including but not limited to medical, dental, vision, life and 401(k). Please note that the job title is subject to change based on the selected candidate’s experience and education.

The work:

Azure Data Engineer with experience integrating data from both internal and external data sources to build a data processing system. Creates new pipelines and builds reusable components at scale to support reporting & analytics data products. Writes complex queries to transform raw data sources into accessible models by coding. Cleans, prepares, transforms, and optimizes data at scale for integration and consumption. Implements data management projects and restructures current web architecture. Solves complex data issues and performs root cause analysis to proactively resolve product issues. Owns the data pipeline and supports systems failures.

Here’s what you need:

• Active Secret security clearance required
• 3+ years of hands-on experience working with Azure cloud native technologies
• 2+ years experience with developing ETL pipelines in Azure using tools such as Azure Data factory, Event hubs, Event grid, Azure functions
• 2+ years developing data engineering pipeline with Databricks or Spark
• 1+ year working with technologies such as CosmoDB, AzureSQL, Redis, Synapse 
• 1+ year working with orchestration tool such as Airflow or Oozie
• Strong programming foundation with python or java
• Strong experience with SQL
• Experience working in Agile teams
• Experience working with streaming and batching solutions
• Experience working with csv, json, xml and complex structures
• Familiar with devOps process, CI/CD, security, coding standard and SDLC cycle

Preferred: 

• Experience with migrations to major cloud
• Experience working with databases, compute, storage and networks on one or more of the following platforms: Azure, AWS, Google Cloud Platform, VMware or Oracle Cloud Platform.

"You must live local to St. Louis, MO OR be willing to travel to St. Louis every week Monday-Thursday"

#clearancejob

The work:

Data Engineer is responsible for creating efficient data pipelines, ensuring data quality, and implementing ETL (Extract, Transform, Load) processes to facilitate the seamless migration and deployment of data across systems. The Data Engineer works within the Agile frameworks to support the team's data-driven approach to application migration, collaborating closely with other team members to ensure data integrity and accessibility throughout the transition to cloud environments.

Key Responsibilities:

• Design and develop scalable, efficient data architectures to support the migration process
• Create data models and schemas that align with both source and target systems
• Develop and maintain robust data pipelines for data generation, collection, and processing
• Implement real-time and batch processing pipelines as required by the migration strategy
• Design and implement ETL processes to migrate data between source and target systems
• Develop scripts and workflows for data extraction, transformation, and loading
• Develop and maintain data cleansing routines to address data inconsistencies
• Create monitoring systems to track data quality metrics throughout the migration
• Collaborate with the migration team to plan and execute data migration strategies
• Provide expertise on data-related aspects of the migration process
• Troubleshoot and resolve data-related issues during migration
• Optimize data processing and storage for improved performance in cloud environments
• Monitor and tune data pipelines for efficiency
• Implement data security measures to protect sensitive information during migration
• Ensure compliance with data protection regulations and organizational policies
• Develop and maintain data access controls and audit trails
• Create and maintain comprehensive documentation for data architectures and processes
• Work closely with data analysts, cloud engineers, and application developers
• Integrate data solutions with other components of the migrated applications
• Participate in Agile ceremonies (Sprint Planning, Daily Stand-ups, Sprint Reviews, and Retrospectives) 

Here's what you need:

• Bachelors degree and a minimum of 5 years experience.
• Active Secret Security clearance required
• Experience with cloud-based ETL services (e.g. AWS Glue, Google Cloud Dataflow, Azure Data Factory)
• Experience with Python, SQL, Spark, and PySpark
• Familiarity with data orchestration and data quality processes 

Preferred experience:

• Experience with data platforms like Databricks, Palantir, and Snowflake

"You must live local to St. Louis, MO OR be willing to travel to St. Louis every week Monday-Thursday"