.AgileEngine is one of the Inc. 5000 fastest-growing companies in the U and a top-3 ranked dev shop according to Clutch.
We create award-winning custom software solutions that help companies across 15+ industries change the lives of millions.If you like a challenging environment where you're working with the best and are encouraged to learn and experiment every day, there's no better place - guaranteed!
: )What you will doDevelop 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 building and 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 thelatest 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 debt items and refactor code as needed.Must haves3+ 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 cloud platforms 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 andcollaboration abilities; Ability to work independently and take ownership of projects; Upper-intermediate English level.Nice to havesExperience 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