ML Engineer

We are looking for a talented ML Engineer who is proficient in a Unix environment and skilled in scripting with Bash and Python. A strong background in AWS infrastructure is essential, with expertise in services such as S3, EC2, Lambda functions, CloudFormation, Athena, DynamoDB, CodeCommit, and SageMaker. You should have significant experience in containerization using Docker and managing containers. A solid software engineering skill set is crucial, including code review and a good understanding of coding best practices. Familiarity with versioning (using Git/CodeCommit), code quality and optimization, error handling, logging, monitoring, validation, and alerting is required. You must be fluent in writing well-tested, readable code capable of processing large volumes of data and ML jobs, employing Infrastructure as Code (IaC) to build data pipelines. Expert knowledge of Python and basic machine learning libraries such as NumPy, SciPy, Pandas, Dask, PyTorch, and TensorFlow is required. A proven track record of linking data from multiple systems for scalable, productionized solutions with a focus on security and monitoring best practices is necessary, along with hands-on experience in the DevOps lifecycle, tools, and frameworks. Cloud security best practices and experience with ML approaches such as supervised/unsupervised machine learning, reinforcement learning, and Bayesian inference are desirable. An AWS certification and experience with Google Cloud's Big Data tools would be a strong advantage. Familiarity with Kafka is a plus but not essential.