Responsibilities
- Develop and maintain complete machine learning workflows, from data input through model deployment and ongoing performance tracking.
- Automate processes for training, evaluating, and releasing models using continuous integration and continuous delivery standards.
- Operate and fine-tune infrastructure for serving models, using container technologies, orchestration platforms, and code-defined infrastructure across cloud providers.
- Guarantee consistent tracking and replication of data, features, and models throughout their lifecycle.
- Set up monitoring solutions to detect changes in model accuracy, response time, data distribution shifts, and performance decline.
- Work closely with data scientists to convert experimental models into robust, scalable services.
- Oversee the full model lifecycle, including scheduled retraining, fallback procedures, experimentation frameworks, and silent deployment strategies.
- Enforce adherence to data protection, security protocols, and ethical AI guidelines.
Benefits
- Competitive pay and rewards structure offering a stake in company performance.
- Work-life balance supported by adaptable schedules and a hybrid remote/in-office model.
- Full health insurance coverage.
- Active support for mental and physical wellness through an internal employee-led committee.
- Frequent team gatherings such as seasonal company events.
- Access to up-to-date hardware and software tools for optimal productivity.
- Dedicated funding for skill advancement and career growth based on performance reviews.
- Chance to contribute to a rapidly expanding global SaaS organization transforming a conventional sector.
- Flat organizational structure with a strong, inclusive workplace culture.
Compensation
Competitive compensation and incentive package
Work Arrangement
Hybrid working policy with flexible hours
Team
Global, fast-growing SaaS company with a non-hierarchical culture
Other
- Visa sponsorship is not offered for this position.
- Applicants must provide legal proof of work authorization in the country.
- Flexible working hours with a hybrid working policy.
- Provision of current technology to support high-quality work output.
- Allocated learning budget for each employee's professional growth.
