Machine Learning Engineer

You will be joining Pie’s MLOps team as a Machine Learning Engineer, you will be a key contributor to our agile team with a focus on ML development tooling and productionizing feature generation pipelines and predictive models. Your primary responsibility will be the development, deployment, and maintenance of our state-of-the-art Machine Learning platform, but your role will extend beyond this. You will leverage your experience in data engineering and model development to help guide best practices, reusability, and simplification on the path to production. Leveraging knowledge and experience with advances in NLP, LLMs and other pretrained models, you will build and optimize scalable machine learning solutions across a wide variety of applications.

In this multifaceted role, you'll collaborate with product managers, data scientists, and data engineers to prioritize, build, implement, and support our ML platform. Understanding their data requirements and operational challenges, you will devise tailored solutions and ensure their successful deployment in the cloud using sound engineering practices.

Your role within the MLOps team is integral, with a particular emphasis on machine learning libraries, feature engineering, validation, and monitoring.  Your contributions will directly impact the robustness, efficiency, and scalability of our ML infrastructure.

• Collaborate with product managers, data scientists, and data engineers to build, implement, and support a world-class ML Platform. This includes participating in designing, training, testing, and deploying ML models, and ensuring their optimal performance in production.
• Design, build, and maintain scalable machine learning solutions and data pipelines, keeping in mind performance and scalability. Use your skills in feature engineering to collaborate with data engineers and data scientists for creating reusable feature stores.
• Monitor the performance of machine learning models in production, debugging and resolving issues as they arise.
• Foster a culture of collaboration and knowledge sharing, educating team members about Machine Learning Engineering/ MLOps best practices like version control for models and data, reproducibility of experiments, evaluation and validation, drift detection and other forms of production monitoring.
• Ensure alignment of ML strategies with the company's broader infrastructure, advocating for compatibility and seamless integration.
• Stay up-to-date with the latest industry trends in machine learning and MLOps, advising on the potential adoption of new tools and techniques.
• Develop full-stack Python applications and service wrappers for delivering predictive services on public cloud infrastructure. Build and manage CI/CD pipelines specific to Machine Learning and Analytic workloads, utilizing Infrastructure as Code (IaC) tools such as AWS CDK, Pulumi, Terraform.
• Manage the full ML platform stack, taking ownership of the complete lifecycle of ML models, from development to maintenance. This includes managing monitoring and data observability.
• Respond promptly and professionally to define, escalate, and resolve all technical issues related to the ML platform stack and the machine learning lifecycle.

• Minimum of 3 years of experience as a MLOps/Machine Learning Engineer, with a track record of designing, implementing, training, and deploying machine learning solutions in a production environment.
• Or 7+ years combination of Data Science, Data Engineer or other related field. 
• Proficient in SQL, Python and Python based data engineering and machine learning libraries and frameworks like TensorFlow, PyTorch, or scikit-learn.
• Robust understanding of MLOps industry best practices and trends, and ability to select appropriate tools and platforms for machine learning operations.
• Additional expertise in a variety of programming and scripting languages including TypeScript, and Bash is preferred.
• Proficiency in building and deploying containerized microservices for pre-processing and model hosting.
• Knowledge of industry-standard ETL/ELT and data orchestration tools, such as Airflow
• Experience with model registries, production-level feature stores, model monitoring, and various SQL/NoSQL Databases.
• Strong problem-solving and analytical skills, with a demonstrated ability to troubleshoot and enhance machine learning models in production.
• Excellent communication, teamwork, with a proven ability to contribute to team efforts on complex technical initiatives and thrive in a fast-paced, Agile environment with production-oriented, incremental release cycles.

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