Senior Machine Learning Engineer

• AI Strategy Development – Partner with the Director of R&D to define and execute the company’s AI strategy, focusing on geoscientific applications.

• Full-Cycle ML Leadership – Manage all aspects of the machine learning lifecycle, from data preprocessing to model deployment and performance monitoring, ensuring a streamlined and effective process.

• Innovative ML Architectures – Design and implement a broad spectrum of machine learning solutions, spanning computer vision, time series forecasting, and geospatial data analysis, while integrating cutting-edge technologies and methodologies.

• MLOps Best Practices – Drive the adoption of robust MLOps frameworks, including CI/CD pipelines for ML models, to enable smooth and scalable AI deployments.

• AI Infrastructure & Optimization – Enhance AI infrastructure and workflows, focusing on performance, scalability, data pipeline efficiency, and automation across all ML processes.

• Cross-Disciplinary Collaboration – Work closely with data engineers, scientists, and geoscientists to establish a well-integrated, end-to-end ML ecosystem within the company.

• Continuous AI Advancement – Regularly improve the efficiency, reliability, and impact of AI-driven systems through iterative optimizations and refinements.

• Geospatial ML Expertise – Familiarity with geospatial databases such as PostGIS and GeoPandas is highly desirable.

Experience:

• At least 7 years of hands-on experience in machine learning engineering, with a strong record of successfully deploying ML solutions into production environments.

Technical Proficiency:

• Expert-level Python programming skills and deep knowledge of ML frameworks, including PyTorch, scikit-learn, and inference engines like ONNX Runtime and OpenVINO.

• Strong grasp of various ML algorithms, architectures, and their real-world applications.

• Experience working with large-scale datasets and cloud computing environments, particularly AWS.

• Proficiency in software engineering best practices, version control systems, and CI/CD methodologies.

• Hands-on experience with containerization, orchestration, and microservices-based architectures.

• Solid understanding of data security, privacy considerations, and compliance requirements in AI-driven applications.

Leadership & Soft Skills:

• Proven ability to lead and mentor ML teams through complex projects.

• Strong analytical and strategic thinking skills to solve challenging AI problems.

• Exceptional communication skills, capable of conveying technical concepts to both technical teams and executive stakeholders.

• Strong project management capabilities, with the ability to oversee multiple initiatives simultaneously.

• Passion for continuous learning and adaptability in the ever-evolving field of machine learning.

Education:

• Master’s or Ph.D. in Computer Science, Machine Learning, or a related discipline. Industry certifications and contributions to the ML community (such as research publications or open-source projects) are a strong plus.