Senior Generative AI Engineer

Key Responsibilities:
Model Development & Deployment:
o Design, train, and optimize generative AI and LLM-based models for diverse
applications within faade engineering and manufacturing workflows.
o Develop computer vision models for faade component recognition, defect
detection, and automated annotation.
o Utilize frameworks like PyTorch or TensorFlow and integrate annotation tools
such as CVAT into the training pipeline.
Cloud Architecture & Integration:
o Implement scalable ML pipelines and model hosting on AWS using services such
as Amazon S3, EC2, SageMaker, Lambda, and ECS/EKS.
o Ensure that deployed models meet performance, availability, and cost
requirements.
Data Management & Experimentation:
o Work with large, heterogeneous datasetsincluding CAD files, images of
assembly drawings, operation manuals, ensuring proper preprocessing and
augmentation.
o Implement MLOps best practices, including CI/CD pipelines, versioning,
monitoring, and automated retraining.
Cross-Functional Collaboration:
o Collaborate with Virtual Construction Lab team membersengineers, architects,
and material scientiststo understand project needs and translate them into AIdriven solutions.
o Clearly communicate complex AI concepts and results to non-technical
stakeholders, providing actionable insights.
Research & Innovation:
o Keep pace with the latest advancements in generative AI, LLMs, and computer
vision.
o Experiment with cutting-edge models and techniques (e.g., diffusion models,
transformer-based architectures, self-supervised learning) to enhance capabilities.
Qualifications & Skills:
Education & Experience:
o Bachelors or Masters degree in Computer Science, Machine Learning, Data
Science, Engineering, or a related field.
o Minimum 5+ years of professional experience in ML/AI, with at least 2+ years
focused on generative models, LLMs, or advanced computer vision solutions.
Technical Expertise:
o Proven track record in training and deploying advanced deep learning models at
scale.
o Proficiency in Python and ML frameworks like PyTorch or TensorFlow.
o Solid experience with AWS ML services (SageMaker, ECS, Lambda) and
MLOps frameworks.
o Hands-on experience with computer vision annotation tools (e.g., CVAT) and
related data labeling workflows.
Soft Skills:
o Strong analytical, problem-solving, and collaboration skills.
o Ability to communicate effectively with both technical and non-technical stakeholders.