MLOps Engineer

TechWish

Vienna, VA

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.
dhere 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)
bility to bridge data science and engineering teams

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Vacancy posted 4 days ago

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