MLOps Engineer

Element Technologies is focused on enhancing solutions through MLOps strategies. The MLOps Engineer will develop and implement end-to-end strategies for machine learning and deep learning models, ensuring robust pipeline maintenance and collaboration with cross-functional teams to address complex business problems.

Responsibilities:

• Develop and implement end-to-end MLOps strategies to enhance solutions, including building, testing, and deploying machine learning and deep learning models
• Design, build, and maintain robust machine learning pipelines for production environments, ensuring seamless integration with operational processes
• Process and transform source data for machine learning pipelines, utilizing cloud computing platforms to enhance efficiency and scalability
• Collaborate with cross-functional teams to assess and apply AI technologies to address complex business problems, focusing on practical implementations and operationalization
• Communicate technical findings and insights to stakeholders and work closely to develop actionable solutions that meet customer needs
• Develop and maintain comprehensive code and model documentation, and support model governance and compliance approvals
• Adhere to best coding practices and standards in Python, including effective use of GitHub for version control and collaborative development
• Prepare and deliver presentations, including written reports and visual presentations, to communicate analysis results and recommendations to leadership

Requirements:

• 5+ years of experience in machine learning and data science, with a focus on operationalizing models and managing MLOps workflows
• 5+ years of hands-on experience with Python, classical machine learning methods, and deep learning frameworks such as Scikit-learn, PyTorch, TensorFlow
• 5+ years of experience leading MLOps projects, demonstrating strong technical communication skills and technical leadership
• Experience with NLP techniques, including text embedding, text classification, and the use and evaluation of LLMs/generative AI models
• Experience with distributed computing frameworks such as Apache Spark
• Experience with distributed machine learning model training using AzureML or databricks platforms
• Expertise in building and tuning weighted model ensembles in online learning contexts
• Experience in forking and modifying open-source projects to meet specific needs
• Proven track record of working on collaborative software projects using GitHub
• Extensive programming experience with Python and PySpark
• Experience with machine learning and deep learning frameworks: Scikit-learn, Pytorch, Tensorflow
• Experimentation skills (MLflow, Optuna, etc.)
• Proven production ML delivery (MLOps, CI/CD)
• Cloud‑native deployment experience (Azure/Databricks preferable)
• Ability to bridge data science and engineering teams