AI Platform Engineer

at  Itemize Corp

ABOUT ITEMIZE

Itemize is the leading AI company automating financial document processing. Its customers include financial organizations, banks, and enterprise offices of the CFO. The company’s market-leading technology automates clerical tasks that still require manual effort. Its solutions elevate teams to focus on higher-impact activities. Itemize delivers efficiencies, intelligence, and new value from financial document automation.
Consumer financial systems have transitioned to digital technologies, but business-to-business processes remain primarily stuck in analog mode. B2B still relies on paper documents, emails, and PDF images for communications and transactions between buyers, sellers, and third parties. These formats require human intervention in numerous tasks. They include data entry, reconciliations, verification, QA, approvals, custom spreadsheets, fraud screening, and compliance. Human involvement drives up errors and omissions, increases operating costs, and leaves significant value on the table.
Unlike Generative AI and legacy technology like OCR and RPA, Itemize AI is designed specifically for financial document processing. Its focus is understanding financial document data to derive intelligence, validate results, and automate workflows. These processes include reconciliation, account coding, compliance, cash flow management, and payments.
Itemize operations are cloud-based, with teams distributed nationwide and internationally. We have teams coast to coast within the United States as well as in several countries worldwide. We welcome people who enjoy the challenge of a fast-paced, high-growth environment on the leading edge of applying vertical AI to B2B financial challenges.

REQUIRED SKILLS AND EXPERIENCE

• Education: Bachelor’s degree in Computer Science, Data Science, or a related field; advanced degree (e.g., MS, PhD) preferred
• Experience: Minimum of 8 years of experience in Data Science, Machine Learning, and AI, with at least 5 years in a leadership role within a SaaS or technology-driven company
• Technical Skills:
• Strong skills in Python and SQL
• Experience with AWS Compute services and managing AWS pipelines (CodePipeline)
• Hands-on experience with Dask for parallel computing
• Expertise in GPU-based AI model processing
• Familiarity with vector databases and data integration techniques
• Strong understanding of LLMs and data voting models
• Problem-Solving: Excellent problem-solving skills with the ability to make data-driven decisions and optimize AI models for production
• Collaboration: Strong communication and collaboration skills to work effectively with data scientists, engineers, and other stakeholders

PREFERRED QUALIFICATIONS:

• Experience in scaling AI-driven products from concept to production in a fast-paced environment
• Demonstrated success in leveraging data analytics to drive business decisions and improve product performance
• Understanding of financial industry technology processes, priorities, and challenges, and customer needs in B2B SaaS markets
• Entrepreneurial mindset with a track record of innovation and driving transformative change

The AI Platform Engineer will be at the forefront of our AI initiatives, taking cutting-edge models developed by our data scientists and transitioning them into a robust production environment. This role involves owning the production processing environment, ensuring high performance, reliability, and scalability. Responsibilities include optimizing AI models, implementing validation tweaks, and managing rules engines. The engineer will work extensively with vector databases, handle GPU-based AI model processing, and develop and maintain AWS pipelines, including CodePipeline. Collaboration is key, as the engineer will work closely with data scientists, engineers, and other stakeholders to drive innovation and efficiency within our AI platform.

This role will report to the VP of Technology and will be responsible for:

• Productionalizing AI Models: Collaborate with data scientists to transition AI models from development to production, ensuring scalability, robustness, and efficiency
• Platform Ownership: Take full ownership of the production processing environment, managing and maintaining the AI platform to ensure high performance and reliability
• Model Optimization: Make necessary tweaks for model validation, rules engine implementation, and improvements in large language models (LLMs) and data voting models
• Data Integration: Work with vector databases and ensure seamless data integration and retrieval for AI models
• MLOps: Develop and manage ML infrastructure on AWS, using tools like CodePipeline, to automate and streamline the deployment process
• GPU Processing: Utilize GPUs for efficient AI model processing, leveraging tools like Dask for parallel computing and optimization
• Technical Expertise: Apply expertise in Python, Transformers, Computer Vision, SQL, and AWS Compute to solve complex technical challenges and enhance the AI platform