Machine Learning Engineer

Why you?

As a Pythian Machine Learning Engineer, you will focus on building and optimizing machine learning pipelines, deploying models to production, and ensuring their scalability and reliability. You will be responsible for integrating machine learning models into various products and client solutions, with an emphasis on utilizing pre-trained models like Large Language Models (LLMs) and other AI-driven technologies. Your role will involve collaboration with cross-functional teams to develop and maintain robust, efficient, and scalable machine learning systems.

What will you be doing?

• 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.

What do we need from you?

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field. Ph.D. is a plus.
• 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.

What do you get in return?

• Love your career: Competitive total rewards package with excellent take home salaries, shifted work time bonus (if applicable) and an annual bonus plan!
• Love your development: Hone your skills or learn new ones with an annual training allowance; 2 paid professional development days, attend conferences, become certified, whatever you like!
• Love your work/life balance: 3 weeks of paid time off and flexible working hours. All you need is a stable internet connection!
• Love your workspace: 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!
• Love your community: Blog during work hours; take a day off and volunteer for your favorite charity.