Full-Time Senior Machine Learning Engineer

• Architecting and designing scalable software systems on AWS Cloud, contributing to both high-level strategy and hands-on implementation.
• Building, deploying, and maintaining AI models and training pipelines, ensuring performance, reliability, and alignment with business goals.
• Creating comprehensive design documentation, including both high-level and low-level specifications, and translating these into clean, maintainable code.
• Collaborating cross-functionally with data scientists, product managers, and fellow engineers to develop elegant, efficient solutions, while engaging a variety of stakeholder throughout the process.
• Leveraging AWS services such as Bedrock, SageMaker, S3, DynamoDB, RDS, Lambda, ECS, and Terraform. Experience with Batch, Step Functions, and API Gateway is highly regarded.
• Demonstrated expertise in Python, containerization (Docker), and the AWS ecosystem, with hands-on experience supporting high-traffic environments focused on scalability, reliability, and performance.
• Strong understanding of professional software engineering practices, including the full software development lifecycle: coding standards, code reviews, CI/CD pipelines, source control, build processes, testing, and operational support.
• Proven ability to translate business questions into data-driven solutions, using quantitative techniques and available data to deliver meaningful outcomes.
• Familiarity with common machine learning techniques (e.g., regression, decision trees, clustering, embeddings, image classification, object detection), and practical experience applying them to real-world problems.
• Experience or interest in Generative AI technologies, such as prompt engineering, vector databases, hybrid search, function calling, and evaluation pipelines. Bonus points for experience deploying AI/ML models using frameworks like TensorFlow, PyTorch, SKLearn, or MLOps tools such as Metaflow, Kubeflow, or MLflow.

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