Senior Applied AI/ML Engineer

We're building production AI/ML capabilities into aviation maintenance and operations software used globally by airlines, MROs, and CAMOs. You'll work on real problems with real operational data — predicting component failures, surfacing anomalies in live operations, mining decades of maintenance records, and getting models to run reliably in environments where downtime affects flight schedules. This is hands-on engineering: model development, data pipelines, deployment, and monitoring. Not advisory, not prompt-tuning a chatbot. About OASES: Our Aviation group delivers mission-critical software solutions that empower airlines, airports, and aviation service providers to operate efficiently and safely. From maintenance planning and flight operations to compliance and resource management, our products enable customers to optimize performance, reduce costs, and ensure regulatory compliance. As part of a global network of specialized software companies, our aviation teams operate with entrepreneurial agility while benefiting from shared expertise, innovation, and best practices across the wider Valsoft group. About Valsoft Corp: Established in Canada in 2015, Valsoft has grown to a global portfolio of 120+ companies, acquiring and developing vertical market software companies, enabling each business to deliver the best mission-critical solutions for customers in their respective industries. A key tenet of Valsoft’s philosophy is to invest in well-established businesses and foster an entrepreneurial environment that moulds companies into leaders in their respective industries. Valsoft looks to buy, hold, and create value through long-term partnerships with existing management. Key Responsibilities: Predictive maintenance — remaining-useful-life models for components, failure prediction from sensor and event data. Anomaly detection — surfacing operational deviations across fleets in real time. Maintenance NLP — extracting structured signal from years of free-text maintenance logs and defect reports. Operational forecasting — parts demand, planning load, workflow bottlenecks. AI-assisted knowledge access — RAG and search over technical manuals, regulatory documentation, and internal operational knowledge. Productionisation — getting all the above to run reliably, observably, and at scale. Requirements: We are looking for someone who understands how AI systems are built — not only how AI tools are used. Strong experience in several of the following areas: Machine learning model development and training. Time-series analysis and predictive analytics. LLMs, RAG pipelines, and vector search. Operational telemetry and data pipelines. MLOps and scalable AI deployment. Python and backend engineering. API and distributed systems design. Strong advantages: aviation, predictive maintenance, industrial IoT, time-series forecasting at scale, or production LLM deployment. We are looking for an engineer capable of building operational AI systems that: Learn from operational data. Improve real-time decision-making. Integrate into production aviation workflows. Scale reliably in operational environments. Why Join Us: This is an opportunity to help shape the future of operational aviation software and real-time operational intelligence across a global aviation software portfolio.