ML Ops Engineer

Your Profile:

• Proven Expertise: You have hands-on experience in AI/ML and Advanced Analytics, with a special focus on deploying and maintaining AI/ML models and services in a production environment.
• Core Competencies: You are skilled in AI/ML application development, testing, serving, monitoring, and troubleshooting.
• Model Reproducibility: You know how to ensure ML models are reproducible and interpretable.
• Deployment Experience: You have independently packaged and deployed AI/ML services to production.
• Post-Deployment Maintenance: You are experienced in monitoring and maintaining AI/ML services after deployment.
• Proficiency in Python: You are highly skilled in Python and have 5+ years of experience working with AI/ML standard libraries such as pandas, scikit-learn, and xgboost.

Nice-to-Haves:

• Familiarity with data processing libraries and frameworks (e.g., pydantic, pandera).
• Experience with web frameworks (e.g., FastAPI, Flask).
• Proficiency in CLI frameworks (e.g., Typer, Click).
• Knowledge of MLOps tools and frameworks (e.g., MLFlow, Azure ML Studio).
• Experience with version control tools for ML datasets and models (e.g., DVC, Azure ML Dataset).
• Familiarity with monitoring libraries and solutions (e.g., NannyML, Evidently AI).
• Expertise in distributed processing libraries (e.g., Ray, Dask, PySpark).
• Experience with pipeline-building and orchestration libraries (e.g., Metaflow, ZenML, Kedro, Airflow, Dagster).
• Knowledge of general Python development tools (e.g., pytest, coverage, tox, mypy, black, ruff, uv, pip-compile).
• Comfort with writing both object-oriented and functional code, and an understanding of concepts such as (de)coupling, coherence, inheritance, and composition.
• You ensure that the code you and your colleagues write is thoroughly tested (unit, integration, end-to-end, and stress/performance).
• You are an advocate for data validation and regularly use type hints.
• You know how to transform a Jupyter notebook into a production-grade piece of code.
• You are familiar with packaging Python applications or libraries for distribution.
• Proficient GIT user: You can collaborate with multiple developers across various repositories while adhering to best practices related to branching, merging, and code reviews.
• You have a solid understanding of Machine Learning algorithms, especially their applications in NLP.
• You have experience with at least one Cloud Provider, preferably Azure Cloud.
• You are proficient with Unix/Linux command line tools and scripting (e.g., shell, bash).
• VIP club membership: You have at least once run rm -rf on production data.

This profile represents an ideal candidate for a Senior AI/ML Engineer who will work on deploying AI models and services at scale, ensuring that they perform optimally in production environments, while maintaining high standards of reproducibility, monitoring, and code quality.