MLOps Engineer

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