Machine Learning Engineer

Summary

Join Pythian as a Machine Learning Engineer and focus on building, optimizing, and deploying machine learning pipelines and models 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; implementing model monitoring; and working with software engineering teams. 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 compensation 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!