Senior MLOps Engineer (Full Stack, React)

Senior MLOps Engineer (Full Stack, React)

Fractal Analytics helps global Fortune 100 companies power every human decision in the enterprise by bringing analytics and AI to the decision.

We're committed to bringing passion and customer focus to the business.

Fractal is seeking a Senior MLOps Engineer to join our dynamic Data Science team within our online retail division at a leading tech product company. The ideal candidate will have a strong background in machine learning operations, with the ability to productionalize ML models, including marketing mix models (MMM), experimentation frameworks, and voice of customer (VoC) analytics, at scale. Additionally, the candidate should be skilled in guiding data engineers to build robust ETL pipelines and possess extensive experience with AWS cloud platforms.

Responsibilities:

1. Model Productionalization: Develop, deploy, and maintain machine learning models (MMM, experimentation, VoC) in production environments to ensure scalability and reliability.
2. Pipeline Development: Collaborate with data engineers to design and build efficient ETL pipelines for data ingestion, transformation, and loading; familiarity with e-commerce Adobe analytics data is preferred.
3. Automation and Monitoring: Implement automated monitoring and alerting for ML models and data pipelines to ensure performance and stability.
4. Collaboration: Work closely with backend data engineers, product, and data science team to develop and ship new, innovative user experiences and performance data visualizations and optimization in retail.
5. Optimization: Continuously optimize ML model performance and infrastructure to improve speed, accuracy, and cost-efficiency.
6. Documentation: Write and review end-user and technical documents, including requirements and design documents for existing and future data systems, as well as data standards and policies.
7. Cloud Management: Leverage AWS services to build, deploy, and manage ML models and data pipelines, ensuring best practices in cloud security and cost management.
8. Mentorship: Guide and mentor data engineers and other ML ops engineers in the team to improve efficiency with data pipelines and MLOps best practices.
9. Innovation: Stay updated with the latest trends and advancements in MLOps, machine learning, and cloud technologies to drive continuous improvement and innovation within the team.
10. Model Tracking and Experimentation: Enable model tracking, experimentation, and automation to streamline the ML development lifecycle.
11. MLOps Component Development: Develop MLOps components using tools like MLFlow, Kubeflow Model Registry, and E2E PaaS/SaaS solutions like DataRobot, HopsWorks, or Dataiku.
12. Lifecycle Management: Work across all phases of the model development lifecycle to build and integrate MLOps components.
13. Knowledge Base: Build and maintain a knowledge base to deliver increasingly complex MLOps projects on the Cloud (AWS, Azure, GCP) or on-premises environments.
14. Client Engagement: Be an integral part of client business development and delivery engagements across multiple domains.

Qualifications:

1. Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
2. Experience: Minimum of 5 years of experience in MLOps, with a proven track record of productionalizing ML models at scale in a commercial setting.
3. Technical Skills:

1. Proficiency in Python, Scala, or Java, and experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
2. Strong experience with AWS services, including but not limited to SageMaker, EC2, S3, Lambda, and CloudFormation.
3. Familiarity with containerization technologies such as Docker and Kubernetes.
4. Expertise in building and maintaining ETL pipelines using tools such as Apache Airflow, Glue, or similar, and familiarity with e-commerce data and Adobe analytics preferred.
5. Knowledge of version control systems like Git and CI/CD tools such as Jenkins or GitLab CI.
6. Experience with model repositories like MLFlow or Kubeflow Model Registry.
7. Familiarity with ML platforms like Kubeflow, DataRobot, HopsWorks, or Dataiku.
8. Experience with large-scale data warehouse solutions such as Teradata, Snowflake, or Redshift.
9. Solid technical database knowledge (Hadoop, Teradata, Snowflake data modeling) and experience optimizing SQL queries on large data.
10. Familiarity with continuous integration & development and automation tools such as Jenkins, Artifactory, and Git.

Soft Skills:

1. Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.
2. Strong communication and interpersonal skills to effectively collaborate with cross-functional teams and stakeholders.
3. Proven ability to mentor and guide junior team members.
4. Ability to present complex ideas in a clear, concise way.
5. Hunger and passion for learning new skills.

Preferred Qualifications:

1. Experience with marketing analytics, experimentation frameworks, and voice of customer analytics.
2. Familiarity with big data technologies such as Spark or Hadoop.
3. Certifications in AWS or related technologies.
4. Experience with Agile and Test-Driven Development methodology.
5. Familiarity with Tableau.

Pay: The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. A reasonable estimate of the current range is: up to $135,000 USD. For the current performance period, you may be eligible for a discretionary bonus.

As a full-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plans in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of employment with the Company. In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides for 11 paid holidays and 12 weeks of Parental Leave. We also follow a “free time” PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation.

Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!