Sr ML Engineer - Remote
# Sr ML Engineer - Remote **ITtude** · North Macedonia · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Machine-Learning-Engineer]` `[Senior-ML-Engineer]` `[MLOps-Engineer]` `[AI-Engineer]` `[Remote-Machine-Learning-Engineer]` --- ### About the Role We are looking for a senior-level AI/ML professional to lead the productionization, deployment, and continuous optimization of ML systems in mission-critical environments. This role requires strong technical leadership, deep hands-on experience shipping models to production, and the ability to drive reliability, scalability, and cost/performance trade-offs end-to-end. The ideal candidate combines strong ML depth with MLOps and production engineering excellence, and can operate autonomously while influencing architecture and best practices across teams. Key Qualifications - Master’s, PhD, or equivalent experience in Computer Science, Engineering, Data Science (or related), with strong foundations in mathematics, statistics, and ML. - 6+ years (or equivalent) experience across ML engineering and MLOps /production ML, with a proven track record delivering high-impact systems in production. - Demonstrated expertise deploying models to production: scalable serving patterns, model packaging, inference optimization, observability, and safe rollout strategies. - Strong experience with model performance and efficiency optimization: latency/throughput tuning, batching, quantization, ONNX/TensorRT, GPU/CPU optimization, memory and cost tuning. ● Strong proficiency in Python. - Hands-on experience with ML frameworks such as PyTorch, scikit-learn, and optimization/runtime stacks like ONNX, TensorRT. - Strong grasp of CI/CD for ML (e.g., GitLab CI), containerization (Docker), and reproducible workflows. - Experience with monitoring and reliability: metrics/logging/tracing, model/data drift, alerting, SLOs, incident response practices. - Kubernetes, AWS, FastAPI (and/or similar Python serving frameworks), SageMaker, service meshes, feature stores, model registries. Key Responsibilities - Handle the production lifecycle of ML systems: training, packaging, deployment, monitoring and controlled rollouts. - Design, build, and continuously improve training and retraining pipelines (data ingestion, labeling/QA loops, feature generation, experiment tracking, reproducibility). - Build and improve high-performance inference services and deployment patterns. - Drive inference optimization (ONNX/TensorRT, quantization, batching, GPU/CPU tuning) with measurable latency/cost improvements. - Partner with DevOps/SRE to ensure operational excellence: scaling, reliability, incident readiness, observability, and alerting. - Own model versioning, CI/CD, and release workflows ensuring reproducibility and safe delivery to production. - Strengthen data & model monitoring (performance, drift, quality) and implement feedback loops for continuous improvement. - Communicate results and trade-offs to stakeholders, turning technical outcomes into business value. - Ensure model quality through rigorous evaluation and production-grade validation aligned with evolving data and business needs. Originally posted on Himalayas
- Machine-Learning-Engineer
- Senior-ML-Engineer
- MLOps-Engineer
- AI-Engineer
- Remote-Machine-Learning-Engineer
Наблюдалась 2026-10-17, впервые 2026-10-17, источник — Himalayas (JSON API).