ML Ops Engineer

Job Description

Role Summary The ML Ops Engineer will be responsible for designing building and maintaining the infrastructure and processes for deploying and managing machine learning models in production Responsibilities Understand and translate business and functional needs into machine learning problem statements Translate complex machine learning problem statements into specific deliverables and requirements Design and develop scalable solutions that leverage machine learning and deep learning models to meet enterprise requirements Translate machine learning algorithms into productionlevel code Collaborate with development teams to test and deploy machine learning models Monitor the performance of deployed models track data or concept drift and update or retrain models as needed Ensure adherence to performance standards and compliance with data security requirements Keep abreast with new tools algorithms and techniques in machine learning and work to implement them in the organization Proficient in Python scripting and familiarity with Python packaging (e.g. PyPI, pip, virtualenv) Hands on experience with ML workflow tools like MLflow, Kubeflow, MLRun, DVC, Airflow leveraging Python integrations. Automate model training, testing and deployment processes using CI/CD tools. Experience with cloud platform preferably GCP and their Python SDKs. Experience with SQL and Python-based ETL processes. Familiarity with data processing frameworks like Apache, Spark or Dask using PySpark or similar Python interfaces. Collaborate with development teams to test, optimize ML workflows and deploy/integrate machine learning models into applications. Design and develop scalable solutions that leverage machine learning and deep learning models to meet enterprise requirements Translate machine learning algorithms into production-level code Knowledge of version control systems (e.g. git) and collaborative coding practices. Monitor the performance of deployed models, track data or concept drift, and update or retrain models as needed Ensure adherence to performance standards and compliance with data security requirements Keep abreast with new tools, algorithms and techniques in machine learning and work to implement them in the organization Education A bachelors degree in computer science data science applied mathematics software engineering or related masters degree preferred Specialization in applied machine learning or machine learning infrastructure preferred Experience 5-7 years of experience in developing and deploying enterprise scale machine learning solutions in a software engineering adjacent field Experience developing and debugging in Python Exposure to architectural patterns of largescale software applications Experience with REST API development in Python (e.g. Flask, Fast API) for model serving. Experience in developing, trouble shooting and resolving issues related to ML systems in production. Understanding of DevOps principles and exposure to architectural patterns of large-scale software applications Required skills Knowledge of working on any Cloud Environment GCP preferred Proficiency in deploying machine learning algorithms as production ready API services Advanced programming skills with Python Ability to effectively communicate technical concepts and results to technical and business audiences in a comprehensive manner Ability to collaborate effectively across multiple teams and stakeholders including analytics teams development teams product management and operations Ability to work independently and in a fully remote environment Willingness and ability to stay up to date on new MLAI technologies and their potential impact on the company