Senior Machine Learning Engineer

The role

Core responsibilities

• Lead machine-learning projects from problem definition and data preparation through deployment, monitoring, and improvement.
• Translate business, product, operational, and technical requirements into machine-learning solutions.
• Design end-to-end pipelines for training, validation, inference, deployment, and monitoring.
• Develop predictive, deep-learning, and other machine-learning solutions for relevant business applications.
• Prepare complex datasets through analysis, preprocessing, feature engineering, transformation, sampling, and enrichment.
• Train, evaluate, optimize, and validate models using systematic experimentation.
• Build production-ready services, APIs, inference pipelines, and supporting software.
• Deploy models across cloud, on-premises, edge, or distributed computing environments.
• Establish CI/CD, automated testing, experiment tracking, model versioning, monitoring, retraining, and rollback processes.
• Investigate production failures, coordinate remediation, and maintain technical documentation.
• Ensure machine-learning systems address privacy, security, compliance, bias, fairness, explainability, and responsible-AI requirements.
• Mentor technical professionals, conduct reviews, and establish engineering standards.

Ideal candidate

• Strong experience in machine learning, artificial intelligence, deep learning, data science, MLOps, or machine-learning engineering.
• Strong understanding of machine-learning theory, algorithms, statistics, and optimization.
• Proven experience developing and deploying production-grade machine-learning systems.
• Strong knowledge of supervised, unsupervised, and deep-learning methodologies.
• Experience with classification, regression, forecasting, ranking, recommendation, anomaly detection, or optimization models.
• Strong Python programming skills and production-quality software development experience.
• Experience with major machine-learning frameworks such as PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, or equivalents.
• Experience with complex datasets, model evaluation, MLOps, lifecycle management, monitoring, and automated deployment.
• Familiarity with cloud platforms, containers, APIs, distributed computing, and production infrastructure.
• Strong knowledge of CI/CD, automated testing, software development practices, and version control.
• Strong understanding of data quality, bias, fairness, explainability, and responsible AI.

Technical scope

• The role covers classification, regression, forecasting, recommendation, ranking, anomaly detection, optimization, natural language processing, computer vision, and intelligent automation where applicable.
• The engineer may evaluate foundation models, generative AI, embeddings, multimodal models, and emerging machine-learning technologies.
• The role includes model monitoring for performance, data drift, concept drift, latency, availability, and operational health.
• The role includes technical feasibility assessments, benchmarking, infrastructure evaluation, and research-to-production work.

Performance focus