Build robust AI algorithms and neural networks to solve targeted business and operational challenges within the DB ecosystem

Use frameworks like PyTorch and Vertex AI to create models for tasks such as anomaly detection, predictive analytics, NLP, and automation of DB processes

Package models into scalable APIs and deploy them into production environments integrated with DB systems and services

Continuously monitor and tune AI/ML models to improve accuracy, scalability, and efficiency using large, complex datasets

Partner with DB engineers, data scientists, product managers, and business stakeholders to align AI initiatives with strategic objectives

Ensure the quality and integrity of training data using best practices in data cleaning, transformation, and validation

Stay up to date with advancements in AI/ML, emerging tools, and frameworks to propose innovative applications of AI within the DB team

Diagnose and resolve model or system issues, improve inference speed, and enhance system performance

Continuously evaluate and validate datasets to ensure reliability and trustworthiness in AI-driven outputs

Model human-like decision-making or automate intricate processes using intelligent systems that interface with data infrastructure

3+ years of relevant experience in AI/ML, data engineering, or similar fields

Proficiency in Google Gemini, Vertex AI, and PyTorch with relevant project experience

Competency in BigQuery, Apache Airflow, and ELK Stack for workflow orchestration, data analytics, and monitoring

Background in building and deploying AI models, APIs, and machine learning pipelines into production environments

Experience with large-scale data processing, cleaning, and transformation

Capability to troubleshoot AI systems, conduct diagnostics, and optimize system performance

Understanding of generative AI models and practical applications to solve real-world business challenges

Ability to communicate effectively in both written and spoken English (B2 level or higher)