Work with a team of machine learning engineers and data scientists to develop robust machine learning model pipelines, architect and implement APIs, and create microservices focused on optimizing latency, availability, and overall performance.
Implement best practices for version control, code review, testing, and documentation, fostering a culture of high-quality software development
Stay current with the latest tools, technologies, and best practices in machine learning engineering and cloud-based infrastructure, and drive continuous improvement within the team
Monitor, troubleshoot, and optimize the performance of machine learning models and related infrastructure
Embrace agile development methodologies, uphold best practices, and seize ongoing learning opportunities.
Engage in collaborative efforts with cross-functional teams, including product managers and engineers, to ensure the delivery of superior-quality products.
Requirements & Skills:
Bachelor’s degree in Computer Science, a related technical discipline, or equivalent hands-on experience.
A minimum of 4 years of industrial experience in deploying machine learning models.
Experience with the following languages (Java/Kotlin, Python, Scala) and preferably ML frameworks (sci-kit-learn, TensorFlow, PyTorch)
Experience with microservice-based architecture, preferably with AWS tooling (SageMaker, DynamoDB, Athena, Glue, etc.)
Experience in software engineering best practices and tools including object-oriented programming, test-driven development, CI/CD, git, shell scripting, task orchestration
Profound knowledge of model deployment, orchestration (Apache airflow), scaling, and managing CPU/GPU resources efficiently.
Exceptional problem-solving, analytical skills, and the ability to tackle complex problems with a critical thinking approach.
Outstanding communication and interpersonal skills, coupled with a demonstrated ability to work collaboratively within a team environment.
Foundational knowledge in statistical concepts (e.g. classification, regression, etc) and deep learning algorithms (e.g. CNN, RNN) is desirable
Experience with generative model-based pipelines from concept to production.
