The Machine Learning Engineer is responsible for developing, implementing, and productizing machine learning models that deliver business value. This role focuses on transforming machine learning prototypes into scalable, production-grade solutions. The ML Engineer works closely with data scientists, software engineers, and product teams to ensure that AI/ML models are effectively integrated into products, services, and applications, enabling data-driven decisions, and enhancing customer experiences.
What You Will Be Doing
- Productize ML Models: Work alongside data scientists who originate and design machine learning models to take these prototypes from concept to production-ready solutions. Focus on building scalable and efficient models for integration into live environments.
- End-to-End Model Deployment: Help deploy machine learning models into production environments, ensuring that they function reliably and are easily maintainable. This includes some DevOps practices such as CI/CD pipelines for model deployment.
- Data Preparation and Feature Engineering: Work with data scientists to preprocess, clean, and transform data to prepare it for production-grade machine learning models.
- Collaborate with Product Teams: Support product managers and stakeholders in defining model requirements and ensuring alignment with product goals.
- Model Optimization and Scaling: Assist in optimizing machine learning models to improve performance and scalability for large-scale production environments.
- Build and Maintain ML Pipelines: Help build and maintain ML pipelines to automate model training, testing, and deployment, incorporating DevOps principles to streamline processes.
- Monitoring and Maintenance: Monitor deployed models, check for performance drift, and work on necessary model updates, integrating monitoring tools for tracking model health.
- Model Evaluation and Reporting: Assist in evaluating model performance, analyzing metrics, and suggesting improvements.
- Documentation and Best Practices: Support documentation efforts related to model deployment processes, performance metrics, and best practices.
- Stay Current: Learn about emerging trends in AI/ML technologies and apply new methods to improve product offerings.
Your Qualifications
- Experience: 0-2 years of experience in machine learning engineering or a related field.
- Education: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field.
- Technical Expertise: Basic understanding of machine learning algorithms, deployment practices, and integration.
- Product Focus: Some exposure to integrating machine learning models into products or production systems.
- Enthusiasm for AI/ML: Passion for AI/ML and a desire to contribute to solving business problems through machine learning.
- DevOps Exposure: Familiarity with DevOps practices in model deployment, including CI/CD pipelines, version control, and automated testing.
A few things we have to offer:
- Competitive compensation
- Great Healthcare + Dental + Vision
- Flexible PTO
- Culture of support, encouraging Life-Work balance
- 401k match
- FSA and HSA options
- Employee Assistance Program
- Paid Parental Leave
- Representing a company with 4,000+ clients and a 99% retention rate
- Accelerated title and salary growth potential
- A fun and energetic work environment that makes you excited to go to work every day
Cleo Communications, LLC is an equal opportunity/affirmative action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability status, protected veteran status or any other characteristic protected by law.
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