Machine Learning Engineer

We are seeking a talented Machine Learning Engineer to join our growing team. As a Machine Learning Engineer, you will play a pivotal role in optimizing ML models for efficient training and inference, deploying deep learning models in specialized hardware for inference usage, monitoring the performance and latency of deployed models, and maintaining ML infrastructure.

What you'll do

• Designing, building, supporting, and scaling our cloud and/or on-premise ML infrastructure.
• Owning and improving workflow orchestration, model execution pipelines, and automated deployment workflows.
• Deploying deep learning models in production environments and optimizing their performance for inference on either GPU or CPU.
• Monitoring deployed ML models for their performance, latency, and throughput using automated tools for monitoring and reporting.
• Driving reliability, scalability, and reproducibility across the ML platform, including containerization, CI/CD, and dependency management.
• Evaluating and improving data science processes, identifying opportunities for automation, efficiency, and scalability.
• Collaborating with other teams, including product managers, data scientists, software engineers, data annotators, and business stakeholders, to ensure successful deployments of ML models.
• Staying up to date with the latest trends and advancements in ML engineering, ML models, and applying this knowledge to enhance the team s capabilities.
• Exploring and learning new technologies that can complement or replace our current stack to improve it.

Qualifications

• Bachelor's or Master's degree in Computer Science or a related field.
• +2 years of experience in a similar role.
• Proficiency in one or more programming languages (e.g., Python, C, C++) with the ability to learn new languages.
• Experience with relational databases, including SQL queries, database definition, and schema design.
• Experience with deploying Deep learning frameworks (e.g. Tensorflow, Pytorch, Onnx) in production environments using inference frameworks (e.g. Nvidia Triton, TFXServing, TorchServe).
• Uphold best practices and principles around clean code, version control, testing, continuous integration and continuous deployment.
• Effective communication skills to convey technical solutions to end-users.
• Experience with monitoring ML models and reporting tools (e.g. grafana, and/or promethues).
• Experience with containerization technologies (e.g. Docker) is highly preferred.
• Experience with distributed computing systems is a plus.
• Experience with cloud platforms (e.g. AWS, GCP, OCI) is a plus.
• Knowledge of big data platforms like kafka, hadoop, and spark is a plus.
• Experience with workflow orchestration tools (e.g., Airflow, Prefect, Dagster, or internal equivalents) is a strong plus.