Senior Back-End/MLOps Engineer
New
T
TruelogicReal Estate, Technology
Location: Santo Domingo; Workplace: RemoteFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of professional software engineering experience
- Required Skills
- Backend DevelopmentPythonSQLJavascriptMachine LearningRubyTypeScriptGoMLOps
Requirements
- 5+ years of professional software engineering experience, with strong backend or full-stack development experience.
- Strong backend development skills and proficiency in at least one of Go, Python, Ruby, or JavaScript/TypeScript.
- Hands-on experience with MLOps or production machine learning systems, including tools such as Vertex AI, MLflow, Kubeflow, or equivalent.
- Experience with feature stores, ideally Chalk or a comparable technology.
- Strong SQL skills and experience working with data and feature pipelines.
- Experience working with cloud platforms such as GCP, AWS, or Azure, with GCP preferred.
- Solid understanding of machine learning concepts and model deployment.
- Strong debugging skills, ownership mindset, and excellent communication.
- Ability to collaborate effectively with cross-functional teams and colleagues across different time zones.
Responsibilities
- Build and maintain full-stack features across marketplace products, including search, ranking, and user-facing experiences.
- Develop and support ML Ops infrastructure, including model deployment pipelines, feature stores, real-time data pipelines, and monitoring.
- Collaborate with data scientists and engineers to bring machine learning models into production reliably and at scale.
- Write clean, well-tested, maintainable code and actively participate in code reviews.
- Contribute to technical planning and break down complex problems into well-scoped, executable tasks.
- Identify and resolve performance, reliability, and scalability issues across existing systems.
- Work with data and feature pipelines, SQL, and experimentation/A/B testing frameworks.
- Communicate progress clearly, surface blockers early, and collaborate effectively with distributed teams across time zones.
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