Machine Learning Engineer

QuadSci.ai is an AI product company focused on enhancing customer experiences through data-driven insights. The Machine Learning Engineer will analyze diverse data sets, deploy and monitor AI products, and collaborate with customers to improve their GTM performance.

Responsibilities:

• Field deployed AI/ML Engineer working across 1 - 5 customers
• Analyzing diverse and dynamic data sets across application telemetry, CRM, Support, Accounting / Billing, Website Analytics and other common enterprise data sources
• Utilize our auto feature engineering assets focused on large telemetry data sets (TBs to PBs)
• Deploy, train, test and monitor AI products at scale within a Customers’ operating architecture in platforms such as Vertex AI
• Engage with other QuadSci deployed colleagues on the explanation of data insights, confirmation of design requirements, root cause analysis, etc
• Work with Customers on AI feature roadmaps (incl. GenAI applications) for their models and ongoing performance management of deployed AI models
• Collaborate with QuadSci colleagues, Customer Cloud Ops and Partners on ML ops, integrations and performance management
• Contribute to QuadSci codebase for auto-feature engineering, AI product packaging, next best action logic and more

Requirements:

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics or other related field
• 3+ years of relevant experience in data science or product development
• Experience with data science and cloud computing environments like VertexAI, Sagemaker or similar tool
• Demonstrated success in the use of clustering, classification, regression, decision trees etc to deliver transformative insights
• Strong experience in building, training, and tuning predictive machine learning, especially in the domains of next best offer or recommended action based on time-series type data sets
• Experience in Natural Language Processing (NLP) for sentiment analysis and behavioral modeling
• Hands on experience with Pandas, Polars and / or Dask (Coiled) for data transformation & feature engineering
• Masters degree in Computer Science, Data Science, Applied Statistics or a related field
• Deployment architecture experience for MLOps optimization using technologies like Dagster or similar tool
• Experience in the use of Large Language Models (LLMs, both Open Source & Proprietary), Embeddings algorithms, Vector DBs and APIs to create GenAI applications
• Familiarity with technologies and/or data architectures such as: Pendo, Salesforce and Open Telemetry
• Direct experience in the use of AI/ML to automate essential business processes that have an impact to Field teams or Customers