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.
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