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**What you will do**- Develop efficient, clean, and maintainable Python code for machine learning pipelines, leveraging our in-house libraries and tools; - Collaborate with the team on code reviews to ensure high code quality and adhere to best practices established in our shared codebase; - Contribute to buildingand maintaining our MLOps infrastructure from the ground up, with a focus on extensibility and reproducibility; - Take ownership of projects by gathering requirements, creating technical design documentation, breaking down tasks, estimating efforts, and executing with key performance indicators (KPIs) in mind; - Optimize machine learning models for performance and scalability; - Integrate machine learning models into production systems using frameworks like SageMaker; - Stay up-to-date with the latest advancements in machine learning and MLOps; - Assist in improving our data management, model tracking, and experimentation solutions; - Contribute to enhancing our code quality, repository structure, and model versioning; - Help identify and implement the best practices for ML services deployment and monitoring; - Collaborate on establishing CI/CD pipelines and promoting deployments across environments; - Address technical debtitems and refactor code as needed.
**Must haves**- **3+ years** of experience in **machine learning engineering** or a related role; - Strong proficiency in **Python** programming; - Experience with machine learning frameworks such as **PyTorch, TensorFlow, or scikit-learn**; - Familiarity with cloudplatforms like **AWS**, including services like **SageMaker, S3, and Secrets Manager**; - Experience with data processing, cleaning, and feature engineering for structured and unstructured data; - Knowledge of software development best practices, including version control (Git), testing, and documentation; - Excellent problem-solving and debugging skills; - Strong communication and collaboration abilities; - Ability to work independently and take ownership of projects; - Upper-intermediate English level.
**Nice to haves**- Experience with Infrastructure as Code (IaC) tools, preferably Pulumi or Terraform; - Experience with classification models and libraries such as XGBoost, SentenceTransformers, or LLMs; - Knowledge of data versioning, experiment tracking, and model registry concepts; - Familiarity with data pipeline and ETL tools like Dagster, Snowflake, and DBT; - Experience with monitoring logs, metrics, and performance testing for batch inference workloads; - Contributions to open-source machine learning projects; - Experience with deploying and monitoring machine learning models in production