Dimitrios Dagdilelis
ML/AI Engineer
Biography
AI/ML Engineer with a PhD in multi-modal data fusion. I empower organizations and teams to navigate their AI transformation journeys. By delivering impactful, high-value data products, I ensure that business strategies are backed by real, scalable AI solutions while separating genuine advancements from overhyped, unreliable claims. My focus is on delivering robust, production-ready systems that solve real-world problems and drive sustainable growth.
Interests
- Multimodal AI
- Computer Vision & LLMs
- MLOps
- Scalable AI
- Explainable AI
Education
PhD in Multi-Modal Data Fusion, 2024
Technical University of Denmark
MSc Automation & Robot Technology, 2020
Technical University of Denmark
Diploma in Electrical & Computer Engineering, 2016
Aristotle University of Thessaloniki
Experience
Senior AI/MLOps Engineer
Danish Defence
- Designed and implemented a centralized AI platform supporting multilingual synopsis generation, content globalization through translation, and internal knowledge discovery using agentic RAGs.
- Leading the design of the image processing platform, delivering AI-generated operational insights about millions of data points to stakeholders within the first 6 months.
- Building services within the AI platform for fine-tuning, hosting LLMs and multimodal models, ingesting and maintaining vector stores, incorporating human feedback, and serving inference for production-scale traffic.
- Using MLOps to enable distributed model training and deploying API endpoints for multiple data modalities.
- Minimizing dependency on labeled data by implementing auto-labeling, self-supervised learning, and accelerating model development with efficiency and cost-effectiveness.
- Align, integrate and scale AI technologies with operational requirements, addressing operations challenges.
- Prototyping state-of-the-art ML solutions.
AI R&D Engineer
- Developing 3D multimodal perception algorithms, enabling downstream collision avoidance, trajectory planning and situational awareness.
- Using generative modeling for realtime 3D semantic representation of the environment.
- Engineered robust sensor fusion frameworks by integrating data from LiDAR, cameras, radar, and HD maps, improving navigation safety and reliability.
- Designed AI diagnostics to counter GNSS spoofing and jamming, leveraging multi-sensor fusion and statistical change detection methods.
- Led research initiatives and mentored engineering teams, fostering collaboration with academic and industrial partners, in order to advance the development of autonomous maritime technologies.
Visiting AI Engineer
- 3D perception algorithm development.
- Using AI to build situational awareness at sea.
- Large-scale vision dataset curation.
- Optimizing labeled data utilization.
PhD
Technical University of Denmark (DTU)
- Autonomous navigation
- Autopilot perception
- Multimodal Data Fusion
- Cyber-resilient Navigation
- Computer Vision
- Deep Learning
- Fault-detection
Featured Publications
Recent Publications
(2024).
Maritime Multi-modal Multi-view Bird Eye View Scene Segmentation.
Under review.
(2024).
GreenHopper: The Danish Spearhead Towards Autonomous Waterborne Mobility.
J. Phys.: Conf. Ser. 2867.
(2022).
Analysing Cyber-resiliency of a Marine Navigation System using Behavioural Relations.
ECC 2022.
(2022).
Cyber-resilience for marine navigation by information fusion and change detection.
Ocean Engineering.