Title: MLOps Engineer
Engagement Type:
FTE
Grade: 7
Location:
REMOTE
Primary 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.
Required Qualifications:
- 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.
Preferred Qualifications:
- 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
Tags & focus areas
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