Lead the technical design and architecture of complex, scalable AI/ML systems and infrastructure within the HR Engineering domain, ensure high reliability, performance, and long-term sustainability.
Drive the development of advanced algorithms for HR applications like talent acquisition and engagement using Python and Google’s internal frameworks, including TensorFlow and JAX, to solve challenges.
Architect robust data pipelines using Google’s infrastructure (e.g., Beam, Dataflow) for large-scale data ingestion, cleaning, and complex feature engineering, while leading root cause analysis for system troubleshooting.
Own the full MLOps lifecycle, including deployment, monitoring, and optimization of production models, while establishing team-wide best practices for software development processes and high-quality code.
Collaborate with cross-functional partners to translate business needs into technical roadmaps, provide mentorship to engineers and drive the strategic adoption of emerging AI technologies across the organization.
Minimum qualifications:
Bachelor’s degree or equivalent practical experience.
5 years of experience with software development in one or more programming languages.
3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Preferred qualifications:
Master’s degree or PhD in Computer Science or a related field with a focus on Artificial Intelligence (AI) or Machine Learning (ML).
7 years of software experience designing and deploying large-scale AI/ML applications and complex models.
Experience with MLOps, establishing best practices for production model monitoring, maintenance, and lifecycle management.
Experience leading and mentoring engineers, and translating complex business objectives into scalable AI-driven roadmaps.
Experience architecting scalable data pipelines using BigQuery, Beam, or Dataflow, and deploying models on Google Cloud Platform.
Understanding of Python and frameworks like TensorFlow, JAX, and Scikit-learn, with deep statistical modeling knowledge.
