Job Duration:

40 Hours / Week, Permanent position, Full time

Job Duties:

• Collaborate closely with the Chief Product Officer, and other team members to align with the overall platform vision, mission, integrations, scope, and design deliverables.
• Conduct requirement gathering, and participate in architecture design, development, and implementation of the platform.
• Coordinate with data engineering team and backend developers to build and maintain a robust data pipeline for processing electronic datasheets, ensuring seamless integration and data flow.
• Design, implement, and optimize Postgres database tables, adjacency lists for knowledge graphs, stored procedures, and triggers based on evolving feature requirements.
• Develop and deploy scalable, highly available, and reliable microservices-based applications using appropriate software design patterns, ensuring high reusability and maintainability.
• Architect and fine-tune machine learning models, including transformer-based models, to enhance the platform's capabilities in processing and analyzing complex electronic datasheets and industry-specific documents.
• Implement advanced Natural Language Processing (NLP) algorithms, such as Named Entity Recognition (NER) and semantic search, to improve data retrieval and interpretation from unstructured text in datasheets.
• Create and refine custom embeddings to power vector search capabilities, enabling precise and contextually relevant search results from large datasets of electronic components.
• Collaborate with the data science team to integrate AI-driven predictive analytics, forecasting trends in component availability and performance, providing valuable insights to end-users.
• Employ transfer learning techniques to adapt pre-trained models for new tasks, ensuring that the platform benefits from the latest advancements in AI with minimal retraining.
• Develop and deploy AI/ML models on cloud infrastructure (AWS and Azure), optimizing the deployment for scalability, performance, and cost-efficiency.
• Implement automation in data labeling and model training pipelines using tools reducing manual effort and speeding up the development cycle.
• Collaborate with frontend and backend teams to implement solutions.
• Review code developed by other developers and provided feedback to ensure adherence to best practices (e.g., style guidelines, version control, testability, and efficiency).
• Research and leverage new AI tools to create a code review and testing process with minimal human intervention.