Summary

Machine Learning Engineer skilled in deep learning, computer vision, image processing, LMMs, model deployment, and synthetic data generation. Experienced with Python, PyTorch, Docker, HPC/SLURM, cloud (AWS/GCP), and large-scale data processing. First-author peer-reviewed paper (Deep-BrownConrady) in IEEE-TASE. Expertise spans ADAS scene understanding, LiDAR, sensor fusion, 3D simulation, and Git-based workflows.

Education

• Master in Computing Science (Specialization: Data Science)

  2024-07
  Tampere University

  GPA: 4.77/5.00

  Courses: Statistical Methods for Text Data Analysis, Pattern Recognition and Machine Learning, Recommender Systems, Data-Driven Programming, Image and Video Processing, Statistical Inference, Bayesian Analysis
• Computer Science

  2020-06
  FAST National University of Computer & Emerging Sciences (FAST-NUCES)

  GPA: 3.52/4.00

  Courses: Data Science, Computer Vision, Deep Learning, Digital Image Processing, Machine Learning, Artificial Intelligence, Information Retrieval, Natural Language Processing, Data Structures, Algorithms

Work Experience

• Machine Learning Engineer

  2022-10 -
  AILiveSim Ltd

  Building ML systems for simulation-driven computer vision and retrieval; production deployments and synthetic data pipelines.

  • Built an asset description & retrieval pipeline using Unreal Engine metadata; integrated LongCLIP for multimodal encoding; stored embeddings in a Chroma vector database for fast similarity search.
  • Generated realistic synthetic datasets by parameterizing 3D environments; developed an object detection model for maritime environments using synthetic data.
  • Engineered a decomposition technique within ResNet for projection matrices & orthogonal components tailored for PCA analysis.
  • Single-image camera calibration: predicted H-FOV, Brown–Conrady distortion, and computed intrinsic K-matrix.
  • Generated LiDAR point clouds and performed voxelization to enhance simulated scene understanding; leveraged LLaVA/LLaMA to automate scene descriptions.
  • Dockerized inference models; deployed models as AWS Lambda functions for scalable serverless execution; reduced operational overhead.
  • Industrial Master’s Thesis: “Prediction of Camera Calibration and Distortion Parameters Using Deep Learning and Synthetic Data.”
• Teaching Assistant — Pattern Recognition and Machine Learning

  2023-09 - 2023-11
  Tampere University

  TA for Prof. Joni-Kristian Kämäräinen; weekly sessions, grading, and feedback for ML topics.

  • Supported assignments on neural networks, decision trees, Bayesian learning, and reinforcement learning; evaluated and graded student work.
• Machine Learning Researcher

  2023-02 - 2023-10
  Amplon Oy

  NLP for strategic planning software; microservices & cloud deployment.

  • Built NLP algorithms to refine business objectives in Hoshin Kanri software; ensured measurability & alignment with strategy.
  • Developed a Flask REST API on Google Cloud Run to suggest improvements for objectives; created an AI microservice to refine KPIs.
• Machine Learning Engineer

  2021-11 - 2022-08
  Ladar Ltd.

  Sensor-fusion computer vision; motion detection; multi-modal training data pipelines.

  • Explored BlenderProc for RGB/Depth segmentation; built motion-triggered capture for live camera feeds.
  • Modified YOLOv5 for 5 channels (RGB + LiDAR depth + IR), improving precision/recall; built Dash interface for result visualization.
  • Set up visual & thermal data collection from IP cameras deployed in Oslo, Norway.
• Project Analyst

  2020-10 - 2021-11
  Offshore Navigation Ltd.

  Optimization & API integration for voyage planning; ML collaboration on weather accuracy.

  • Worked on VoyOpt sail-planning optimization; integrated APIs for global ship positional information.
  • Collaborated with ML team to improve weather data accuracy strategies.
• Data Science Intern

  2020-05 - 2020-08
  Offshore Navigation Ltd.

  Geospatial data wrangling & analysis for maritime applications.

  • Processed large multidimensional datasets using Xarray & NetCDF4; improved processing speed & accuracy.
  • Generated vessel position heatmaps from Marine Traffic data for route planning insights.
• Teaching Assistant — Artificial Intelligence

  2020-01 - 2020-07
  National University of Computer & Emerging Sciences (FAST-NUCES)

  TA for AI course; assignments, quizzes, and student support.

  • Designed assignments, graded quizzes, and conducted sessions to resolve course-related queries.

Skills

Publications

• Deep-BrownConrady: Prediction of Camera Calibration and Distortion Parameters Using Deep Learning and Synthetic Data

  2025
  IEEE Transactions on Automation Science and Engineering

  First-author peer-reviewed article presenting a deep learning approach for single-image camera calibration and distortion parameter estimation; includes results on synthetic data and production use. DOI: 10.1109/TASE.2025.3588584.

  View Publication

Teaching

• Pattern Recognition and Machine Learning

  2023
  Tampere University

  Role: Teaching Assistant (Graduate-level course)

  Weekly sessions, assignment assistance, and grading across neural nets, decision trees, Bayesian learning, and RL.
• Artificial Intelligence

  2020
  FAST-NUCES

  Role: Teaching Assistant (Undergraduate course)

  Designed assignments, graded quizzes, and supported student learning in AI fundamentals.