Teman Data is a growing company in Data & AI, helping clients manage, analyze, and leverage their data with modern cloud and automation solutions.

Role Overview:

We’re looking for a hybrid Machine Learning Operations 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
• Strong Python programming skills (profiling, debugging, writing efficient code)
• Hands-on experience deploying ML models (REST APIs, microservices, or batch inference)
• Familiarity with at least one ML framework (PyTorch, TensorFlow, scikit-learn)
• Understanding of software engineering fundamentals: OOP, testing, version control (Git)
• Experience with Docker and cloud platforms (AWS, GCP, or Azure)

Big Plus:

• Go programming experience (for building lightweight, high-performance services)
• Knowledge of model optimization techniques (quantization, pruning, ONNX conversion)
• Experience with orchestration tools (Kubernetes, Airflow, Kubeflow)