Senior Machine Learning Engineer

About Us

We are an international organization focused on creating intelligent technologies and scalable machine learning solutions to enhance business performance, efficiency, and customer experience across various industries. Our cross-functional teams integrate AI, Data Science, Software Engineering, and more to convert complex datasets into dependable machine learning systems that deliver tangible business outcomes.

Role Overview

We are looking for a Senior Machine Learning Engineer with extensive experience to lead the design, development, deployment, and ongoing management of production-grade machine learning systems. The role demands a blend of expertise in machine learning, deep learning, software and data engineering, and MLOps to build scalable models and intelligent applications, establish robust development workflows, and translate advanced analytics into effective business solutions.

Key Responsibilities

• Lead end-to-end machine learning projects, from defining problems and preparing data to model creation, deployment, monitoring, and iterative enhancement.
• Convert requirements from business, product, operational, and technical teams into applicable machine learning solutions.
• Select appropriate machine learning methodologies based on objectives, available data, constraints, and desired results.
• Architect and implement comprehensive ML pipelines covering training, validation, inference, deployment, and system monitoring.
• Create supervised, unsupervised, semi-supervised, and reinforcement learning models as necessary.
• Develop predictive models for tasks such as classification, regression, forecasting, recommendation, ranking, anomaly detection, and optimization.
• Design and build deep learning models using suitable architectures and frameworks.
• Handle diverse datasets including structured, unstructured, time series, text, images, and audio.
• Perform exploratory data analysis to identify patterns, relationships, anomalies, and data quality concerns.
• Develop data preprocessing, feature engineering, transformation, sampling, and enrichment workflows.
• Maintain high-quality datasets for training, validation, and testing purposes.
• Analyze dataset attributes like coverage, class imbalance, missing data, outliers, bias, and shifts in distribution.
• Implement strategies such as data augmentation, synthetic data generation, sampling, and hard example mining.
• Train, fine-tune, evaluate, and enhance machine learning models.
• Conduct hyperparameter tuning and systematic experimental testing.
• Define and apply model evaluation criteria and validation procedures.
• Assess model performance with metrics including accuracy, precision, recall, F1 score, ROC-AUC, calibration, ranking, forecasting, among others.
• Perform error analysis to investigate false positives/negatives, performance drift, instability, and other issues.
• Use validation techniques such as cross-validation, statistical tests, sensitivity analyses, and robustness checks.
• Compare various algorithms, architectures, features, datasets, and modeling strategies.
• Optimize models for diverse goals like accuracy, generalization, latency, memory efficiency, computational cost, and scalability.
• Apply optimization methods like quantization, pruning, distillation, feature selection, and efficient inference as appropriate.
• Develop production-ready ML services, APIs, inference pipelines, and supporting software components.
• Deploy models across cloud, on-premises, edge, or distributed computing platforms.
• Collaborate with software and data engineering teams to integrate models into production systems.
• Implement continuous integration/delivery (CI/CD) and automated testing for ML workflows.
• Manage experiment tracking, model and dataset versioning, and encourage reproducible development.
• Establish monitoring to track model performance, data and concept drift, latency, availability, and operational health.
• Automate model retraining, validation, deployment, and rollback processes where applicable.
• Set standards and governance for ML lifecycle management.
• Ensure thorough documentation covering datasets, features, models, experiments, assumptions, dependencies, and deployment configurations.
• Create dashboards and monitoring tools to oversee model and system performance.
• Investigate and resolve production model failures through root cause analysis and remediation.
• Collaborate with product teams on model requirements, acceptance criteria, performance goals, and release schedules.
• Work with data scientists and researchers to transition experimental models into reliable production-grade solutions.
• Partner with data engineers to build dependable pipelines and scalable feature extraction methodologies.
• Assess new ML techniques and technologies with AI and research teams.
• Support ML applications including recommendation systems, forecasting, personalization, fraud detection, risk modeling, optimization, NLP, computer vision, and intelligent automation.
• Evaluate emerging topics such as foundation models, generative AI, embeddings, and multimodal learning.
• Review open-source and commercial ML frameworks, platforms, models, and infrastructure.
• Conduct proof-of-concept projects and technical viability studies for new ML capabilities.
• Set benchmarks for model accuracy, infrastructure efficiency, and production dependability.
• Ensure compliance with privacy, security, responsible AI, and regulatory standards.
• Evaluate potential biases, fairness, explainability, and unintended side effects of models.
• Implement safeguards for handling sensitive data and ML applications.
• Manage third-party vendors, consultants, data providers, and technical partners when needed.
• Mentor junior ML engineers, data scientists, and other technical team members.
• Perform thorough reviews of code, models, architectures, and designs.
• Define engineering standards, reusable components, development guidelines, and best practices.
• Stay current with advances in ML, AI, MLOps, cloud computing, and related technologies.
• Provide leadership on complex ML architecture and engineering challenges.
• Report regularly to management on model performance, project progress, technical risks, infrastructure needs, and improvement opportunities.

Key Performance Indicators

• Accuracy, precision, recall, F1 score, ROC-AUC of models
• Forecasting and calibration accuracy
• Generalization and robustness of models
• Latency, throughput, availability, and reliability of inference and production models
• Data quality and feature pipeline stability
• Detection of data drift and concept drift
• Rates of model degradation, retraining success, and project delivery efficiency
• Experimentation and prototype-to-production cycle times
• Success rates of model deployment and automated testing coverage
• Monitoring coverage and experiment reproducibility
• Compliance with model versioning and governance
• Infrastructure utilization and compute cost effectiveness
• Reduction in technical debt and improvements in pipeline automation
• Stakeholder satisfaction and business impact from ML solutions
• Adherence to responsible AI, security, and privacy standards
• Completeness of technical documentation and research utilization
• Team development and mentoring effectiveness

Candidate Profile

• Deep expertise in machine learning, AI, deep learning, data science, MLOps, or machine learning engineering
• Experience in sectors like technology, finance, e-commerce, healthcare, logistics, manufacturing, consulting, or similar data-rich fields
• Strong grasp of ML theory, algorithms, statistics, and optimization principles
• Track record of developing and deploying robust production ML systems
• Proficiency in supervised, unsupervised, and deep learning approaches
• Knowledge of models for classification, regression, forecasting, ranking, recommendation, anomaly detection, and optimization
• Advanced programming skills in Python and production-quality software development
• Familiarity with frameworks such as PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, or equivalents
• Hands-on experience with data preprocessing, feature engineering, model training, evaluation, and tuning
• Expertise managing large-scale, complex datasets
• Understanding of model evaluation practices, validation, error analysis, and performance monitoring
• Experience with ML lifecycle management tools including experiment tracking and automated deployments
• Knowledge of cloud environments, containerization, APIs, distributed computing, and production infrastructure
• Experience implementing CI/CD, automated testing, and version control in ML projects
• Familiarity with model monitoring, data and concept drift detection, and retraining processes
• Strong insight into data quality, bias mitigation, fairness, explainability, and responsible AI ethics