Director, Machine Learning Operations Engineering
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Director, Machine Learning Operations Engineering
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Director, Machine Learning Operations Engineering
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At Early Warning, we’ve powered and protected the U.S. financial system for over thirty years with cutting-edge solutions like Zelle, Paze℠, and so much more. As a trusted name in payments, we partner with thousands of institutions to increase access to financial services and protect transactions for hundreds of millions of consumers and small businesses.
Positions located in Scottsdale, San Francisco, Chicago, or New York follow a hybrid work model to allow for a more collaborative working environment.
Candidates responding to this posting must independently possess the eligibility to work in the United States, for any employer, at the date of hire. This position is ineligible for employment Visa sponsorship.
Overall Purpose
This position is responsible leading the team that owns and operates the platforms, tools, and processes that take our models from ideas to production models, serving predictions in real time, and monitoring deployments to ensure quality predictions and stable platforms. The Director, ML Ops will be responsible for leading ML Ops Engineers and coordinating actions, roadmaps, and backlogs with Product Management and senior leadership.
Essential Functions
- Manage team of ML Ops Engineers. Manage day-to-day backlog of activities. Maintain technical excellence.
- Develop strategic direction for ML Ops team, platform, and infrastructure with an eye towards governance, optimization, and automation. Coordinate ML Ops strategy with Enterprise analytics technology and Analytics Data Platform roadmaps.
- Design, build, and maintain scalable ML infrastructure and pipelines for model training, deployment, and monitoring. Identify and implement improvements to existing modeling pipelines, while building next generation tooling to support model deployment
- Optimize orchestration processes to ensure efficient deployment and management of predictive models
- Optimize resource usage to minimize infrastructure expense while maximizing performance
- Monitor and maintain the performance, security, and scalability of the ML infrastructure
- Collaborate with data scientists and software engineers to streamline the ML lifecycle from development to production
- Develop and maintain tools for data analysis, experimentation, model versioning, and artifact management. Support data and model governance requirements as needed.
- Create robust monitoring systems to measure and trend model performance, detect model drift, and ensure optimal performance of models in production
- Develop automation scripts and tools to improve the efficiency and reliability of MLOps processes
- Optimize ML workflows for efficiency, scalability, and reliability
- Provide technical assistance and mentorship to all team members
- Supports the company commitment to risk management and protecting the integrity and confidentiality of systems and data.
Minimum Qualifications
- Bachelor's degree in Computer Science, Engineering, or a related field
- 12+ years experience in Data Science, ML Engineering or ML Ops capacity
- 5 years experience managing highly technical employees such as Data Scientists or Engineers.
- Expert level programming skills in Python and experience with Data Science and ML packages and frameworks
- Deep experience with AWS services and architecture
- Experience implementing and supporting end-to-end Machine Learning workflows and patterns
- Proficiency with containerization technologies (Docker, Kubernetes) and CI/CD practices
- Experience deploying models with MLOps tools such as MLflow, Kubeflow, or similar platforms
- Expert understanding of data management, distributed computing, and software architecture principles
- Proven experience delivering real-time models in production environments
- Experience in hybrid (OnPrem / Cloud) environments
- Hadoop / Hive / Cloudera experience
- Experience with Scala / Java programming languages and modern distributed computing technologies such as Spark
- Background and drug screen.
Early Warning works together in a highly collaborative office environment.Working conditions consist of a normal office environment. Work is primarily sedentary and requires extensive use of a computer and involves sitting for periods of approximately four hours. Work may require occasional standing, walking, kneeling, and reaching. Must be able to lift 10 pounds occasionally and/or negligible amount of force frequently. Requires visual acuity and dexterity to view, prepare, and manipulate documents and office equipment including personal computers. Requires the ability to communicate with internal and/or external customers.
Employee must be able to perform essential functions and physical requirements of position with or without reasonable accommodation.
The Base Pay Scale For This Position In
Phoenix, AZ/Chicago, IL in USD per year is: $190,000-$230,0000.
New York, NY in USD per year is: $210,000 - $250,000.
Additionally, candidates are eligible for a discretionary incentive plan and benefits.
This pay scale is subject to change and is not necessarily reflective of actual compensation that may be earned, nor a promise of any specific pay for any specific candidate, which is always dependent on legitimate factors considered at the time of job offer. Early Warning Services takes into consideration a variety of factors when determining a competitive salary offer, including, but not limited to, the job scope, market rates and geographic location of a position, candidate’s education, experience, training, and specialized skills or certification(s) in relation to the job requirements and compared with internal equity (peers). The business actively supports and reviews wage equity to ensure that pay decisions are not based on gender, race, national origin, or any other protected classes.
Some of the Ways We Prioritize Your Health and Happiness
- Healthcare Coverage –Competitive medical (PPO/HDHP), dental, and vision plans as well as company contributions to your Health Savings Account (HSA) or pre-tax savings through flexible spending accounts (FSA) for commuting, health & dependent care expenses.
- 401(k) Retirement Plan –Featuring a 100% Company Safe Harbor Match on your first 6% deferral immediately upon eligibility.
- Paid Time Off – Unlimited Time Off for Exempt (salaried) employees, as well as generous PTO for Non-Exempt (hourly) employees, plus 11 paid company holidays and a paid volunteer day.
- 12 weeks of Paid Parental Leave
- Maven Family Planning – provides support through your Parenting journey including egg freezing, fertility, adoption, surrogacy, pregnancy, postpartum, early pediatrics, and returning to work.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Early Warning Services, LLC (“Early Warning”) considers for employment, hires, retains and promotes qualified candidates on the basis of ability, potential, and valid qualifications without regard to race, religious creed, religion, color, sex, sexual orientation, genetic information, gender, gender identity, gender expression, age, national origin, ancestry, citizenship, protected veteran or disability status or any factor prohibited by law, and as such affirms in policy and practice to support and promote equal employment opportunity and affirmative action, in accordance with all applicable federal, state, and municipal laws. The company also prohibits discrimination on other bases such as medical condition, marital status or any other factor that is irrelevant to the performance of our employees.
Seniority level
Seniority level
Director
Employment type
Employment type
Full-time
Job function
Job function
Engineering and Information Technology
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#J-18808-Ljbffr- Location:
- San Francisco
- Salary:
- $250,000 +
- Job Type:
- FullTime
- Category:
- Engineering
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