Machine Learning Operations (MLOps) Engineer

About Us

We imagine the new. Inspire the next. And use the power of our creativity to help build up those around us.

At Allison, we provide a limitless environment where you can build, create, and grow. Our openly collaborative and highly supportive culture is free from bureaucracy and red tape. With over 1,000 innovators from diverse backgrounds, we break new ground for world-class clients across 50 global markets and dozens of industries. We believe in creating a space where everyone can freely express their opinions, share their ideas and dreams for the future, and be themselves.

We foster an inclusive culture that attracts builders from all backgrounds who can envision new solutions and create outcomes that move our clients' businesses forward, while helping everyone on the team learn and grow together. Our shared ideal of the builder's mindset is limitless and available to everyone, and we push the boundaries to create new and innovative solutions for our clients and ourselves.

We create lasting impact and relationships, and our culture fosters meaningful connections and friendships that last beyond the workplace. If you're ready to join a team that pushes you to be your best, supports you every step of the way, and celebrates your successes, welcome to Allison.

Overview

We are looking for a skilled Machine Learning Operations (MLOps) Engineer to enhance the automation, scalability, and deployment of machine learning models and data pipelines. This role is ideal for professionals with experience in CI/CD pipelines, cloud computing, Kubernetes, and data pipeline automation. The MLOps Engineer will play a critical role in improving model deployment, monitoring, and operational efficiency.

Responsibilities

Model Deployment & CI/CD Automation

• Design and implement CI/CD pipelines to automate machine learning model development, deployment, and monitoring.
• Refactor Jupyter Notebook-based models into modular Python packages for production-ready ML pipelines.
• Develop containerized ML workflows using Docker and Kubernetes for scalable deployment.
• Automate model testing, hyperparameter tuning, and optimization to improve model performance and reliability.
  
  

Data Pipeline & Engineering

• Build and maintain ETL and data processing pipelines to support machine learning workloads.
• Optimize data extraction, transformation, and loading (ETL) processes for efficiency and scalability.
• Manage batch and real-time data pipelines using Apache Spark, Airflow, and BigQuery.
• Lead the migration of ML pipelines from Vertex AI/GCP to Databricks, improving performance and cost efficiency.
  
  

Cloud Infrastructure & Automation

• Implement and manage cloud-based ML environments on Google Cloud Platform (GCP) and AWS.
• Deploy and monitor machine learning models using Databricks Model Serving, Hugging Face Transformers, and Apache Spark.
• Develop infrastructure-as-code solutions for scalable machine learning deployment.
• Monitor model drift, data integrity, and performance using logging and alerting tools.
  
  

Collaboration & Best Practices

• Work closely with data scientists, engineers, and product teams to streamline model deployment.
• Implement MLOps best practices, including model versioning, reproducibility, and governance.
• Document ML workflows, pipelines, and troubleshooting protocols for long-term maintainability.
• Develop interactive dashboards and monitoring tools for model performance analysis.
  
  

Qualifications

Required Qualifications

• 5 years of experience in machine learning engineering, data engineering, or MLOps.
• Strong expertise in Python, SQL, Airflow, and Spark.
• Hands-on experience with Kubernetes, Docker, and cloud-based ML deployments (GCP, AWS, or Databricks).
• Experience in CI/CD automation for machine learning and data pipelines.
• Familiarity with NLP, clustering algorithms, and statistical modeling.
• Solid knowledge of version control (Git), automation scripting, and model monitoring frameworks.
  
  

Preferred Qualifications

• Experience with Hugging Face Transformers and Apache Spark for large-scale ML workflows.
• Strong understanding of feature engineering, model retraining, and A/B testing.
• Hands-on experience with model serving frameworks like TensorFlow Serving or Databricks Model Serving.
• Experience in financial, advertising, or real-time data processing domains.
  
  

Benefits

• Hybrid work environment with home and office schedule (2 days in office per week) and work from anywhere weeks
• Comprehensive health benefits (healthcare, vision, dental, pet, home, and auto insurance)
• Generous time off policies (unlimited paid time off, wellness days, national holidays, summer Fridays)
• Four-week sabbatical every five consecutive years of employment
• Exceptional parental leave benefits
• Global mentorship and networking programs
• Monthly cell phone reimbursement
• 401k savings and employee stock purchase plan
• Volunteer hours (20 hours annually) for designated non-profit partner and personal choice
• Globally driven IDE A initiatives (Employee Advocacy Groups, Multicultural Center of Excellence)
• Career growth opportunities, such as Allison University (multi-day customized trainings for each level)