**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 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 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 debt items 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 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 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.
**The benefits of joining us**
- **Professional growth**
Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps
- **Competitive compensation**
We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities
- **A selection of exciting projects**
Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands
- **Flextime**
Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office - whatever makes you the happiest and most productive.
**Agile Engine**
AgileEngine is one of the Inc. 5000 fastest-growing companies in the US 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! :)
**Job Types**: Full-time, Contract
Work Location: Remote

📌 Machine Learning Engineer Id27476 (México)
🏢 Agileengine
📍 México