Principal AI/ML Operations Engineer en BlackLine

The Principal AI/ML Operations Engineer leads the architecture, automation, and operationalization of both machine learning and AI systems at scale. This role defines the strategy and technical standards for ML-Ops and AIOps across the organization, ensuring models and agents are evaluated, deployed, governed, and monitored with reliability, efficiency, and compliance. The candidate will collaborate across AI, data, and product engineering teams to drive best practices for serving, observability, automated retraining, evaluation flywheels, and operational guardrails for AI systems in production 

Leadership and Strategy 

• Define enterprise-level standards and reference architectures for ML-Ops and AIOps systems. 

• 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. 

 AI System Deployment and Integration: 

• Lead the deployment of AI models and systems in various environments. 

• Collaborate with development teams to integrate AI solutions into existing workflows and applications. 

• Ensure seamless integration with different platforms and technologies. 

• 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. 

• Collaborate cross-functionally to standardize authentication, authorization, and session governance for multi-agent runtimes. 

Model Deployment and Integration: 

• 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. 

Infrastructure and Environment Management: 

• 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. 

• Work closely with other teams to ensure the availability, security, and performance of machine learning systems. 

Monitoring and Maintenance: 

• 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. 

Automation and Orchestration: 

• 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. 

Collaboration with Data Science Teams: 

• Collaborate with data scientists to understand model requirements and constraints for deployment. 

• Facilitate the transition of machine learning models from research to production, ensuring scalability and efficiency. 

Performance Optimization: 

• Identify and implement optimizations to enhance the performance and efficiency of machine learning models in production. 

• Conduct performance analysis and implement improvements based on resource utilization of metrics. 

Security and Compliance: 

• Implement security measures to protect machine learning systems and data. 

• Ensure compliance with regulatory requirements and industry standards related to machine learning and data privacy. 

• Integrate audit controls, metadata storage, and lineage tracking across ML and AI workflows. 

• Ensure complete monitoring and feedback loops including event logs, evaluations, and automated retraining triggers. 

• Enforce secure deployment patterns with Infrastructure-as-Code and cloud-native secrets management. 

• Define SLAs, error budgets, and compliance reporting mechanisms for ML and AI systems. 

• Education and Experience:  

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field. 

• 10+ years in ML infrastructure, DevOps, and software system architecture; 4+ years in leading MLOps or AI Ops platforms. 

• Technical Skills:  

• Strong programming skills in languages such as Python, Java, or Scala. 

• Expertise 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 management. 

• Strong competencies in CI/CD, IaC, and DevSecOps pipelines integrating testing, compliance, and deployment automation. 

• Hands-on with observability stacks (Prometheus, Grafana, Newrelic) for model and agent performance tracking. 

• Understanding of governance frameworks for Responsible AI, auditability, and cost metering across training and inference workloads. 

• Proficiency in containerization technologies (e.g., Docker, Kubernetes). 

• Operations and Infrastructure: 

• Proficient in scripting languages (e.g., Bash, python) for automation. 

• Experience with workflow orchestration tools (e.g., Apache Airflow). 

• Expertise in managing and optimizing cloud-based infrastructure. 

• Familiarity with DevOps practices and tools for automated deployment. 

• Understanding of

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