Machine Learning Operations Specialist

5 months ago

Marlborough, United States BJ's Wholesale Club Full time

Job Title: Machine Learning Operations (ML Ops) Resource

Join a team of more than 25,000 team members, comprised of our Club Support Center and over 250 clubs and 7 distribution centers in 17 states. We’re committed to delivering value and convenience to our Members, helping them save every day on everything they need for their family and home. BJ’s Wholesale Club offers a collaborative, team-oriented environment where all team members can learn, grow, and excel.


Who You Are:

You are a highly skilled and experienced Machine Learning professional who can blend data science principles and technology to build scalable ML solutions to solve business needs and enhance customer experiences. You excel at building, optimizing the deployment, monitoring, and performance of machine learning models in production environments.


What the Role Is:

As an ML Ops Resource, you will be responsible for building, managing and scaling our machine learning models, ensuring they are production-ready and continuously optimized. You will design and maintain robust ML infrastructure, implement CI/CD pipelines for model deployment, and monitor model performance to provide actionable insights. Your role will involve close collaboration with data scientists, engineers, and IT teams to integrate ML models into production systems seamlessly.


Key Responsibilities:

Model Deployment and Management:

  • Develop and implement scalable ML deployment strategies, ensuring models are production-ready.
  • Automate the deployment process of machine learning models using CI/CD pipelines.
  • Monitor the performance of models in production and optimize them for performance and accuracy.
  • Manage the lifecycle of machine learning models, including versioning, testing, and rollback strategies.

Infrastructure and Tools:

  • Design and maintain robust ML infrastructure, including data pipelines, model serving infrastructure, and monitoring tools.
  • Implement and manage ML platforms and tools to support the end-to-end ML workflow.
  • Ensure the infrastructure can handle large-scale data processing and model training workloads.

Collaboration and Communication:

  • Work closely with data scientists, engineers, and IT teams to ensure seamless integration of ML models into production systems.
  • Collaborate with cross-functional teams to understand business requirements and translate them into ML solutions.

Monitoring and Optimization:

  • Implement monitoring and alerting systems to track the performance and health of ML models.
  • Continuously optimize models and infrastructure for performance, cost, and scalability.
  • Analyze and resolve production issues related to ML models and pipelines.

Security and Compliance:

  • Ensure compliance with data privacy and security regulations in all ML operations.
  • Implement security best practices in ML model deployment and data handling.
  • Maintain documentation and SOPs for ML Ops processes and procedures.

Requirements:

  • Educational Background: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
  • Professional Experience: Minimum of 3-5 years of experience in machine learning or ML Ops.
  • Technical Expertise:
  • Strong understanding of machine learning concepts and algorithms
  • Worked on model management and retraining lifecycle
  • Strong programming skills with Python and PySpark (preferred)
  • Strong knowledge of data processing and ETL pipelines
  • Experience with CI/CD pipelines, automation tools, and practices
  • Proficiency in monitoring tools and performance optimization techniques
  • Experience with engineering and development collaboration tool such as GIT/Jira/Confluence
  • Familiarity with cloud platforms (AWS, Azure, GCP) and containerization technologies

Soft Skills:

  • Problem-Solving: Strong analytical and problem-solving skills.
  • Communication: Excellent verbal and written communication skills.
  • Team Collaboration: Ability to work collaboratively in a cross-functional team environment.
  • Adaptability: Ability to adapt to new technologies and rapidly changing environments.
  • Detail-Oriented: Attention to detail and a commitment to delivering high-quality solutions.

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