Teman Data helps businesses transform by turning data into actionable insights. It provides analytics and AI services, including clustering and domain-specific analysis, to support digital transformation. The work covers multiple domains such as economics, industry, infrastructure, healthcare, employment, government, and education. Teman Data applies data science and machine learning techniques across client data to improve customer behavior understanding, company performance, and operational trends. The company targets business clients seeking data-driven transformation and scalable analytics capabilities.
Teman Data helps businesses transform by turning data into actionable insights. It provides analytics and AI services, including clustering and domain-specific analysis, to support digital transformation. The work covers multiple domains such as economics, industry, infrastructure, healthcare, employment, government, and education. Teman Data applies data science and machine learning techniques across client data to improve customer behavior understanding, company performance, and operational trends. The company targets business clients seeking data-driven transformation and scalable analytics capabilities.
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Teman Data is a growing company in Data & Al, helping clients manage, analyze, and leverage their data with modern cloud and automation solutions.
Job Title: Machine Learning Ops
Role Overview:
We’re looking for a hybrid Machine Learning Ops to bridge the gap between model development and production deployment. Our data science team builds effective models, but we need someone who can harden, optimize, and operate these models at scale. You’ll focus on improving the performance, efficiency, and reliability of our AI services—reducing memory footprint and latency while ensuring robust production operations.
Core Responsibilities:
-Design, build, and maintain scalable inference pipelines and -model deployment infrastructure
-Optimize existing AI services for memory usage and execution speed
-Implement monitoring, logging, and alerting for models in production
-Collaborate with data scientists to refactor research-grade code into production-ready software
-Build CI/CD workflows and containerized (Docker) deployments for ML models
-Troubleshoot and resolve performance bottlenecks in model serving
Must-Have:
-1-3 years of professional experience in software engineering or MLOps
