Simpson Strong-Tie Vietnam

Tóm tắt công việc

Pipeline Takeoff (PTO) is a web-based application that allows you to plan and manage your projects within one cohesive platform. This includes the ability to upload digital blueprints, record measurements, and create fast and accurate estimates.

Document Intelligence is an Azure AI Service that supports PTO tool not only for auto-name sheets but also to validate scales on the house design. Additionally, AI OCR assists users in auto take-off key measures for repeated objects such as doors, lights, etc. Through the integration of AI in PTO, service takeoff challenges can astutely be resolved by leveraging the "potential features" that have been developing.

• Auto-takeoff 1000+ objects (rooms, doors, windows, etc.) concurrently for seconds
• Replace Blue Beam, Planswift, Autosketch.
• Auto-scale recognition
• Auto-detect room boundary & Auto-calculate Area/ Perimeter
• Digitize jobsite plan into room areas.

WHAT YOU’LL BE DOING

At Simpson Strong-Tie we value the individual, and the creativity that individuals can bring to the workplace. You join this company for a career, not a job!

• You will join the AI engineering team at the most innovative manufacturing and construction with US Team to build and develop AI services to help the product market improve its performance.
• You enjoy working in a cross-functional environment, collaborating with product managers, software engineers, designers, data analytics, data engineers, and business stakeholders.
• You will also be responsible for creating and optimizing core modules of the MLOps platform, from the computation layer to the orchestrator layer.
• Working alongside our data scientists, you will enhance their workflows by providing efficient and effective solutions developed by the MLOps team.
• Help coordinate schedules with SST Product Development , the Takeoff Tool Team and Vendor.
• Follow up with Vendor closely to make sure alignment of between PTO and Auto-assit takeoff
• Research, create and deploy AI/ML algorithm

DESIRED SKILLS AND EXPERIENCE

If you can do everything listed above, you’ve got what it takes. Perhaps some of the following would be helpful too:

• Experience of at least 5 years in AI, Machine Learning, Data Science roles.
• Strong technical hands-on experience in Python.
• Design, develop and maintain scalable and resilient AI/ML products, e.g. Recommendations system & Personalization, Chatbot, Search, Forecasting, etc. You are responsible for end-to-end machine learning product life cycle.
• Apply technical expertise with quantitative analysis, experimentation, data mining, and the presentation of data to develop strategies for our machine learning products.
• Solid knowledge of Machine Learning fundamental i.e. knowledge of model’s variant and use cases, end-to-end ML lifecycle process, model development, model selection, hyperparameter optimization, feature engineering, evaluation.
• Strong hands-on experience in Machine Learning algorithms & libraries like Tensorflow, Pytorch, Keras, Scikit, Pandas.
• Monitoring the performance of the MLOps platform and proactively identifying areas for optimization. This includes analyzing system performance, ensuring low latency, and optimizing cloud resource consumption for cost-effective operations.
• Design, implement, and optimize the core modules of our MLOps platform. This includes developing solutions that enhance our machine learning workflows’ scalability, efficiency, and robustness.
• Proven track record of operating independently, stakeholder management, demonstrating creativity, and delivering results.
• Hands-on experience with cloud providers such as Google Cloud Platform (GCP) or Amazon Web Services (AWS) or Microsoft Azure. Your familiarity with cloud services and infrastructure will be essential in architecting scalable and reliable MLOps solutions.
• Practical experience of ML modeling, involving the entire process from conceptualizing ideas to developing, evaluating, and deploying models, with the added advantage of having expertise in monitoring and managing model performance after deployment.
• Good verbal and written communication in English is required.