Senior Software Engineer AI

Senior AI/ML EngineerWe're looking for a Senior AI/ML 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. Responsibilities include:Lead data pipeline development: Build and maintain PySpark ETL pipelines with high data quality and performanceManage integrations: Establish robust connections to client data sources via APIs and tools like FiveTran, Plaid, and BlackLine's own internal connector ecosystemEnsure reliability: Monitor pipeline performance, automate testing, and validate data accuracyOptimize for scale: Implement performance improvements (e.g., CDC mechanisms, indexing strategies) for large-scale datasetsCollaborate & innovate: Work with business stakeholders to refine data requirements and integrate cutting-edge AI and big data technologiesYou'll Get To:Leadership and StrategyPartner 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 environmentsLead incident response and reliability strategies for ML/AI systemsCollaborate with development teams to integrate AI solutions into existing workflows and applicationsEnsure seamless integration with different platforms and technologiesDefine and manage MCP Registry for agentic component onboarding, lifecycle versioning, and dependency governanceBuild CI/CD pipelines automating LLM agent deployment, policy validation, and prompt evaluation of workflowsDevelop and operationalize experimentation frameworks for agent evaluations, scenario regression, and performance analyticsImplement logging, metering, and auditing for agent behavior, function calls, and compliance alignmentCreate scalable observability systems-tracking conversation outcomes, factual accuracy, latency, escalation patterns, and safety eventsArchitect end-to-end guardrails for AI agents including prompt injection protection, identity-aware routing, and tool usage authorizationCollaborate cross-functionally to standardize authentication, authorization, and session governance for multi-agent runtimesModel Deployment and IntegrationArchitect and standardize model registries and feature stores to support version tracking, lineage, and reproducibility across environmentsLead the deployment of machine learning models into production environments, ensuring scalability, reliability, and efficiencyCollaborate with software engineers to integrate machine learning models into existing applications and systemsImplement and maintain APIs for model inferenceInfrastructure and Environment ManagementDesign and manage training infrastructure including distributed training orchestration, GPU/TPU resource allocation, and automatic scalingImplement CI/CD for model workflows using pipelines integrated with model validation, bias checks, and rollback automationBuild 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 cloudWork closely with other teams to ensure the availability, security, and performance of machine learning systemsMonitoring and MaintenanceImplement robust monitoring solutions for deployed machine learning models to detect issues and ensure performanceCollaborate with data scientists and engineers to address and resolve model performance and data quality issuesConduct regular system maintenance, updates, and optimizations to ensure optimal performance of machine learning solutionsAutomation and OrchestrationDevelop and maintain automation scripts and tools for managing machine learning workflowsImplement orchestration systems to streamline the end-to-end machine learning lifecycle, from data preparation to model deploymentCollaboration with Data Science TeamsCollaborate with data scientists to understand model requirements and constraints for deploymentFacilitate the transition of machine learning models from research to production, ensuring scalability and efficiencyPerform performance optimization to enhance the performance and efficiency of machine learning models in productionConduct performance analysis and implement improvements based on resource utilization of metricsSecurity and ComplianceImplement security measures to protect machine learning systems and dataEnsure compliance with regulatory requirements and industry standards related to machine learning and data privacyIntegrate audit controls, metadata storage, and lineage tracking across ML and AI workflowsEnsure complete monitoring and feedback loops including event logs, evaluations, and automated retraining triggersEnforce secure deployment patterns with Infrastructure-as-Code and cloud-native secrets managementDefine SLAs, error budgets, and compliance reporting mechanisms for ML and AI systemsWhat You'll Bring:3+ years of experience with programming skills in languages such as Python, Java, or ScalaExpertise in ML frameworks (TensorFlow, PyTorch, scikit-learn) and orchestration tools (Airflow, Kubeflow, Vertex AI, MLflow)Proven experience operating production pipelines for ML and LLM-based systems across cloud ecosystems (GCP, AWS, Azure)Deep familiarity with LangChain, LangGraph, ADK or similar agentic system runtime managementStrong competencies in CI/CD, IaC, and DevSecOps pipelines integrating testing, compliance, and deployment automationHands-on with observability stacks (Prometheus, Grafana, Newrelic) for model and agent performance trackingUnderstanding of governance frameworks for Responsible AI, auditability, and cost metering across training and inference workloadsProficiency in containerization technologies (e.g., Docker, Kubernetes)We're Even More Excited If You Have:Operations and Infrastructure:Proficient in scripting languages (e.g., Bash, python) for automationExperience with workflow orchestration tools (e.g., Apache Airflow)Expertise in managing and optimizing cloud-based infrastructureFamiliarity with DevOps practices and tools for automated deploymentUnderstanding of network configurations and security protocolsProblem-solving and Critical Thinking:Ability to define problems, collect and analyze data, and propose innovative solutions. Strong critical thinking skills to evaluate models, identify limitations, andAdaptability and Learning Agility:Comfortable working in a fast-paced, rapidly evolving environment. Proactive in staying up to date with the latest trends, techniques, and technologies in AI/data scienceThrive at BlackLine Because You Are Joining:A technology-based company with a sense of adventure and a vision for the future. Every door at BlackLine is open. Just bring your brains, your problem-solving skills, and be part of a winning team at the world's most trusted name in Finance Automation!A culture that is kind, open, and accepting. It's a place where people can embrace what makes them unique, and the mix of cultural backgrounds and varying interests cultivates diverse thought and perspectives.A culture where BlackLiner's continued growth and learning is empowered. BlackLine offers a wide variety of professional development seminars and inclusive affinity groups to celebrate and support our diversity.BlackLine is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity or expression, race, ethnicity, age, religious creed, national origin, physical or mental disability, ancestry, color, marital status, sexual orientation, military or veteran status, status as a victim of domestic violence, sexual assault or stalking, medical condition, genetic information, or any other protected class or category recognized by applicable equal employment opportunity or other similar laws.BlackLine recognizes that the ways we work and the workplace itself has shifted. We innovate in a workplace that optimizes a combination of virtual and in-person interactions to maximize collaboration and nurture