Senior ML Engineer
New
I
ITtudeMachine learning
Location: Skopje, MKFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 6+ years (or equivalent) experience across ML engineering and MLOps /production ML
- Required Skills
- AWSDockerPythonKubernetesPyTorchFastAPIscikit-learn
Requirements
- Master’s, PhD, or equivalent experience in Computer Science, Engineering, Data Science, or a related field.
- Strong foundations in mathematics, statistics, and machine learning.
- 6+ years (or equivalent) of experience across ML engineering and MLOps or production ML.
- A proven track record delivering high-impact systems in production.
- Experience deploying models to production, including scalable serving, model packaging, inference optimization, observability, and safe rollouts.
- Experience optimizing model performance and efficiency, including latency, throughput, batching, quantization, ONNX/TensorRT, GPU/CPU, memory, and cost tuning.
- Strong proficiency in Python.
- Hands-on experience with PyTorch, scikit-learn, ONNX, and TensorRT.
- Experience with CI/CD for ML, such as GitLab CI, Docker, and reproducible workflows.
- Experience with metrics, logging, tracing, model/data drift, alerting, SLOs, and incident response practices.
- Experience with Kubernetes, AWS, FastAPI or similar Python serving frameworks, SageMaker, service meshes, feature stores, or model registries.
Responsibilities
- Handle the production lifecycle of ML systems, including training, packaging, deployment, monitoring, and controlled rollouts.
- Design, build, and improve training and retraining pipelines for data ingestion, labeling and QA, feature generation, experiment tracking, and reproducibility.
- Build and improve high-performance inference services and deployment patterns.
- Optimize inference using ONNX/TensorRT, quantization, batching, and GPU/CPU tuning.
- Partner with DevOps/SRE on scaling, reliability, incident readiness, observability, and alerting.
- Own model versioning, CI/CD, and release workflows for reproducible and safe production delivery.
- Strengthen data and model monitoring for performance, drift, and quality, and implement feedback loops.
- Communicate results and trade-offs to stakeholders.
- Evaluate and validate model quality against evolving data and business needs.
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