• Collaborate closely with internal teams such as Data Science, Data Engineering, Paylocity’s Cloud Center of Excellence (CCOE), DevOps, and Delivery Platforms to understand requirements and ensure alignment of machine learning engineering solutions with overall business objectives and priorities. • Leverage cutting-edge big data technologies on AWS utilizing Databricks and Spark to develop scalable and efficient machine learning solutions for millions of users. • Create automated data and modeling pipelines, collaborating with internal teams to ensure smooth integration and deployment of machine learning software features. • Lead the optimization of CI/CD workflows, ensuring scalability and resilience while addressing complex challenges in automation in partnership with DevOps and Delivery Platforms. • Proactively identify and resolve issues/bugs, ensuring AppSec vulnerabilities are identified and corrected, working closely with Application Security and CCOE teams. • Drive the adoption of best practices in machine learning engineering across teams, contributing to the development of formal training programs and materials for MLE tool adoption. • Actively participate in cross-functional meetings and discussions, providing feedback, commentary, requirements, and questions to ensure alignment and drive project success.

• Collaborate closely with internal teams such as Data Science, Data Engineering, Paylocity’s Cloud Center of Excellence (CCOE), DevOps, and Delivery Platforms to understand requirements and ensure alignment of machine learning engineering solutions with overall business objectives and priorities. • Leverage cutting-edge big data technologies on AWS utilizing Databricks and Spark to develop scalable and efficient machine learning solutions for millions of users. • Create automated data and modeling pipelines, collaborating with internal teams to ensure smooth integration and deployment of machine learning software features. • Lead the optimization of CI/CD workflows, ensuring scalability and resilience while addressing complex challenges in automation in partnership with DevOps and Delivery Platforms. • Proactively identify and resolve issues/bugs, ensuring AppSec vulnerabilities are identified and corrected, working closely with Application Security and CCOE teams. • Drive the adoption of best practices in machine learning engineering across teams, contributing to the development of formal training programs and materials for MLE tool adoption. • Actively participate in cross-functional meetings and discussions, providing feedback, commentary, requirements, and questions to ensure alignment and drive project success.

• Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition) • Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions. • Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX • Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning. You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps. • Production Engineering: Own the path to production. Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS. • Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team.

• Work with AI tools • Generative AI and AI engineering • Agents for AI-driven content production