Job Description

• Design and deploy production-grade ML systems using state-of-the-art tools and heterogeneous data sources for use cases including anomaly detection, time-series forecasting, resource optimization, and predictive maintenance.

• Translate business/application requirements, model specifications, data descriptions, and test criteria into working PoCs, and evolve them into scalable, reliable services.

• Build robust training-to-inference pipelines with hyperparameter optimization, experiment tracking, model versioning, reproducible containerized builds, and automated deployment.

• Work cross-functionally with architects, engineers, and product/pre-sales teams to design experiments, interpret results, and communicate the trade-offs between accuracy, scalability, latency, and cost.

• Apply ML, statistics, optimization, and operations research methods to construct interpretable, automated ML workflows and decision-support systems.

Minimum Requirements

• Education: MSc in Computer Science, Electrical Engineering, Mathematics, Statistics, or related field — or equivalent experience (4+ years designing and deploying ML systems; research experience is a plus).

• Strong grounding in mathematics, probability, statistics, optimization, and ML fundamentals.

• Proficiency in Python and ML tooling (NumPy, pandas, scikit-learn; PyTorch/TensorFlow is a plus).

• Expertise in data preprocessing, feature engineering, and experimental design with sound statistical judgment.

• (Nice to have) Familiarity with MLOps: monitoring, observability, drift detection, CI/CD, deployment patterns, and cost optimisation.

• (Nice to have) Experience with cloud platforms (AWS, GCP, Azure).

• (Nice to have) Knowledge of model efficiency techniques (compression, quantization, distillation) and accelerator-aware inference.

• (Nice to have) Exposure to telecom systems (RAN/OSS/BSS/NWDAF) or industrial OT environments.