Machine Learning Engineer | Data Science & Analytics

About the position

As a Machine Learning Engineer, you'll take ownership of the end-to-end MLOps lifecycle, building and managing scalable solutions that turn complex data into actionable insights. You are responsible for designing, building, deploying, and monitoring machine learning solutions to enhance business operations in areas such as distribution assets and customer solutions. You will create and optimize scalable, resilient systems capable of processing and analyzing large volumes of data, while collaborating with data scientists, architects, and cross-functional teams to design innovative solutions aligned with business needs.

Responsibilities

• Design, build, deploy, and monitor machine learning solutions to enhance business operations.
• Create and optimize scalable, resilient systems capable of processing and analyzing large volumes of data.
• Partner with data scientists, architects, and cross-functional teams to design innovative solutions.
• Conduct code reviews, mentor team members, and implement best practices for software and ML development.
• Develop architecture diagrams, maintain robust documentation, and design workflows to monitor key performance indicators (KPIs).

Requirements

• BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field.
• Minimum six (6) years directly related data analytics, data science, predictive modeling, building and deploying machine learning solutions, or advanced degree and four (4) years directly related experience.
• Strong analytical and problem-solving skills and programming knowledge.
• High level of proficiency in commonly used programming languages and tools like Python, SQL, and cloud solutions.
• Strong communication, presentation and writing skills.
• Ability to lead teams in evaluations and implementation of solutions.
• Ability to work with key internal and external stakeholders and all levels of management.

Nice-to-haves

• Masters or Doctorate degrees in relevant fields.
• 2+ years of hands-on experience with major cloud machine learning and MLOps services in an enterprise setting.
• Familiarity with PyTorch or Tensorflow.
• 1+ year of experience with scaling infrastructure using GPUs or PySpark.
• 2+ years experience with MLOps services including docker, CI/CD, Kubernetes, and building/managing/monitoring pipelines.
• Experience with serving generative AI services.
• Experience in integrating ML inferences with web services.