- Develop and optimize CNN and LLM-powered models for computer vision, document extraction, and automated construction workflows.
- Prototype, fine-tune, and assess models for NLP tasks such as classification, entity recognition, and summarization of construction data.
- Build scalable ML pipelines and backend services that integrate into production-grade agents and digital platforms.
- Drive the end-to-end ML lifecycle: from experimentation and training, to deployment, monitoring, and continuous improvement.
- Integrate retrieval, ML, and rules-based methods to deliver reliable, explainable, and supportable features.
- Collaborate closely with product managers, software engineers, and construction domain experts to solve real-world challenges with measurable business impact.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field.
- 5+ years of experience designing and deploying applied ML systems at scale.
- Experience with computer vision (CNNs, object detection, segmentation) and natural language processing (LLMs, embeddings, transformers).
- Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face).
- Experience with ML Ops platforms and deploying ML systems into production (MLflow, Kubeflow or equivalent).
- Experience with APIs, CI/CD pipelines, cloud platforms (AWS/Azure/GCP).
- Ability to clearly communicate technical concepts to both engineers and non-technical stakeholders.
- Experience applying ML in construction, CAD/BIM, architecture, or digital twin platforms is preferred.
- Familiarity with graph-based retrieval, RAG pipelines, or multimodal ML is preferred.
Core Competencies
Demonstrates expertise in developing and optimizing CNN and LLM-powered models for computer vision and NLP tasks, with a strong focus on building scalable ML pipelines and deploying production-grade systems. Proficient in collaborating with cross-functional teams to deliver impactful solutions in the construction domain.
Highest-signal resume keywords
- Computer Vision (CNNs, Object Detection, Segmentation)
- Natural Language Processing (LLMs, Embeddings, Transformers)
- Python Programming
- ML Frameworks (PyTorch, TensorFlow, Hugging Face)
- ML Ops Platforms (MLflow, Kubeflow)
ATS Optimization Keywords
Hard Skills
- Machine Learning
- Model Deployment
- Data Classification
- Entity Recognition
- Model Fine-Tuning
- Scalable ML Pipelines
- Continuous Improvement
- APIs
- CI/CD Pipelines
- Cloud Platforms (AWS/Azure/GCP)
Soft Skills
- Clear Communication
- Collaboration
Certifications & Qualifications
- Bachelor’s or Master’s Degree in Computer Science, Machine Learning, or Related Field
Industry Keywords
- Construction
- CAD/BIM
- Architecture
- Digital Twin Platforms
- Graph-Based Retrieval
- RAG Pipelines
- Multimodal ML