II Software Engineer AI​/ML Ops

Make Your Mark:
We’re looking for a Lead Data Engineer to design, build, and optimize data pipelines that power our next‑generation AI‑driven accounting agents. You’ll lead the development of scalable, high‑performance data infrastructure while collaborating closely across teams.

• Lead data pipeline development:
  Build and maintain PySpark ETL pipelines with high data quality and performance.
• Manage integrations:
  Establish robust connections to client data sources via APIs and tools like Five Tran, Plaid, and Black Line’s internal connector ecosystem.
• Ensure reliability:
  Monitor pipeline performance, automate testing, and validate data accuracy.
• Optimize for scale:
  Implement performance improvements such as CDC mechanisms and indexing strategies for large‑scale datasets.
• Collaborate & innovate:
  Work with business stakeholders to refine data requirements and integrate cutting‑edge AI and big data technologies.
• Partner with data science, security, and product teams to set evaluation and governance standards (guardrails, bias, drift, latency SLAs).
• Mentor senior engineers and drive design reviews for ML pipelines, model registries, and agentic runtime environments.
• Lead incident response and reliability strategies for ML/AI systems.
• Collaborate with development teams to integrate AI solutions into existing workflows and applications.
• Define and manage MCP Registry for agentic component onboarding, lifecycle versioning, and dependency governance.
• Build CI/CD pipelines automating LLM agent deployment, policy validation, and prompt evaluation of workflows.
• Develop and operationalize experimentation frameworks for agent evaluations, scenario regression, and performance analytics.
• Implement logging, metering, and auditing for agent behavior, function calls, and compliance alignment.
• Create scalable observability systems tracking conversation outcomes, factual accuracy, latency, escalation patterns, and safety events.
• Architect end‑to‑end guardrails for AI agents, including prompt‑injection protection, identity‑aware routing, and tool usage authorization.
• Standardize authentication, authorization, and session governance for multi‑agent runtimes.
• Architect and standardize model registries and feature stores to support version tracking, lineage, and reproducibility across environments.
• Lead the deployment of machine learning models into production environments, ensuring scalability, reliability, and efficiency.
• Collaborate with software engineers to integrate machine learning models into existing applications and systems.
• Implement and maintain APIs for model inference.
• Design and manage training infrastructure including distributed training orchestration, GPU/TPU resource allocation, and automatic scaling.
• Implement CI/CD for model workflows using pipelines integrated with model validation, bias checks, and rollback automation.
• Build standardized experimentation frameworks for reproducible training, tuning, and deployment cycles (MLflow, W&B, Kubeflow).
• Manage and optimize the infrastructure required for machine learning operations in cloud.
• Ensure availability, security, and performance of machine learning systems across teams.
• Implement robust monitoring solutions for deployed machine learning models to detect issues and ensure performance.
• Collaborate with data scientists and engineers to address and resolve model performance and data quality issues.
• Conduct regular system maintenance, updates, and optimizations to ensure optimal performance of machine learning solutions.
• Develop and maintain automation scripts and tools for managing machine learning workflows.
• Implement orchestration systems to streamline the end‑to‑end machine learning lifecycle, from data preparation to model deployment.
• Facilitate the transition of machine learning models from research to production, ensuring scalability and efficiency.
• Identify and implement optimizations to enhance performance and efficiency of machine learning models in production.
• Conduct performance analysis and implement improvements based on resource utilization metrics.
• Implement security measures to protect machine learning systems and data.
• Ensure compliance with…