Staff II Software Engineer AI/ML Ops

Overview In this role you will design and optimize data pipelines powering AI-driven accounting agents, collaborating with cross-functional teams to scale data infrastructure. You will lead the development of high-performance PySpark ETL pipelines and manage data integrations from client sources and internal connectors. Your work supports reliable, scalable data and ML workflows, with governance and security baked into every stage. This is a hands-on leadership role at a company renowned for finance automation and cutting-edge AI. Compensation / Benefits Salary range provided Incentive programs Benefits and wellness plans Hybrid work with office in Pleasanton/New York/Los Angeles Professional development and inclusive culture Responsibilities Lead data pipeline development via PySpark ETL with a focus on quality and performance Manage integrations to client data sources using APIs and tools like Five Tran, Plaid, and Black Line's internal connector ecosystem Ensure reliability through monitoring, automated testing, and data accuracy validation Optimize for scale with CDC, indexing strategies, and large datasets Collaborate with business stakeholders to refine data requirements and integrate AI and big data technologies Key requirements Extensive practical experience delivering business solutions affecting large parts of the organization Proficiency in Python, Java, or Scala Experience with ML frameworks (Tensor Flow, PyTorch, scikit-learn) and orchestration tools (Airflow, Kubeflow, Vertex AI, MLflow)Production experience with ML/LLM pipelines across cloud providers (GCP, AWS, Azure)Familiarity with Lang Chain, Lang Graph, ADK or similar agentic runtimes Strong CI/CD, IaC, and Dev Sec Ops practices Observability stacks (Prometheus, Grafana, New Relic) for model/agent performance Containerization (Docker, Kubernetes)Automation scripting (Bash, Python) and workflow orchestration tools Cloud infrastructure management expertise Security, governance, and compliant AI/ML practices Leadership and mentorship Cross-functional collaboration Problem-solving and strategic thinking PySpark ETLAPIs and integrations (Five Tran, Plaid)ML frameworks (Tensor Flow, PyTorch, scikit-learn)