Machine Learning Engineer

Summary

Join Pythian as a Machine Learning Engineer and focus on building, optimizing, and deploying machine learning pipelines to production. You will integrate machine learning models into various products and client solutions, utilizing pre-trained models like LLMs. Collaboration with cross-functional teams is key to developing and maintaining robust, efficient, and scalable systems. Responsibilities include designing, developing, and maintaining machine learning pipelines; deploying models to production; collaborating with data scientists; optimizing models; integrating models with cloud platforms; and implementing model monitoring systems. The ideal candidate possesses a Bachelor's or Master's degree in a related field, 3+ years of experience, strong programming skills in Python or Java, and experience with cloud platforms and containerization technologies. Pythian offers a competitive total rewards package, professional development opportunities, flexible working hours, and paid time off.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field
• 3+ years of experience in machine learning engineering, software engineering, or a related role
• Strong programming skills in Python, Java, or similar languages, with proficiency in ML frameworks such as TensorFlow, PyTorch, or Scikit-learn
• Hands-on experience with deploying pre-trained models, such as Large Language Models (LLMs), into production environments
• Experience with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes)
• Solid understanding of data pipelines, ETL processes, and version control systems (e.g., Git)
• Experience in building scalable, distributed systems and optimizing machine learning models for performance
• Familiarity with MLOps tools and practices, including model versioning, monitoring, and CI/CD pipelines
• Strong communication skills and ability to collaborate with cross-functional teams, including data scientists and engineers

Responsibilities

• Design, develop, and maintain machine learning pipelines for internal and client-driven projects
• Deploy machine learning models, including pre-trained models (e.g., LLMs), into production environments and ensure scalability and performance
• Collaborate with data scientists to translate models into production-ready systems that meet business requirements
• Optimize and tune machine learning models for performance, reliability, and cost-efficiency
• Integrate machine learning models with cloud platforms and other infrastructure (e.g., AWS, GCP, Azure)
• Implement model monitoring, logging, and maintenance systems to ensure continuous operation and improvement of deployed models
• Work closely with software engineering teams to ensure seamless model integration into larger applications
• Stay up to date with the latest advancements in machine learning engineering, infrastructure, and deployment technologies

Preferred Qualifications

Ph.D. is a plus

Benefits

• Competitive total rewards package with excellent take home salaries, shifted work time bonus (if applicable) and an annual bonus plan!
• Annual training allowance; 2 paid professional development days, attend conferences, become certified, whatever you like!
• 3 weeks of paid time off and flexible working hours
• We give you all the equipment you need to work from home including a laptop with your choice of OS, and budget to personalize your work environment!