Principal Machine Learning Engineer

Principal Machine Learning Engineer (onsite) role focused on building and maintaining production AI and ML systems, from end-to-end architecture through MLOps and monitoring.

Responsibilities

• Shape end-to-end architecture, development, and deployment for AI, including machine learning, GenAI, and agentic models tailored to business use cases
• Design and develop agentic AI systems, including multi-agent orchestration, tool-use, evaluation, and autonomous task-execution pipelines to automate complex enterprise workflows
• Set and enforce model development standards covering data preprocessing, feature engineering, model selection, hyperparameter tuning, evaluation, and documentation
• Work with data engineering teams to deliver robust, scalable, high-quality data pipelines for both training and inference
• Implement organizational MLOps and LLMOps, including CI/CD pipelines for data wrangling, feature engineering, model training, evaluation, deployment, and monitoring
• Define and enforce standards for model versioning, experiment tracking, reproducibility, and model registry management
• Build model monitoring frameworks to detect performance degradation, data drift, concept drift, and bias, including automated alerting and retraining triggers
• Implement AI governance frameworks covering model risk management, fairness, explainability, privacy, and security
• Champion AI principles across the global business with human oversight and accountability embedded across the AI lifecycle

Requirements

• 10+ years building software or machine learning systems, with 6+ years shipping and operating machine learning in production (served, monitored, and maintained after launch)
• Prior experience as the senior-most or near-senior-most engineer on a production ML or data platform, owning architectural decisions others depended on
• Proven track record setting technical standards that persisted beyond your involvement, and leading through influence without formal authority
• Strong hands-on Python and SQL, plus software engineering fundamentals for maintainable ML systems at scale: version control, automated testing, code review, modular design, and enforcement across teams
• Expert hands-on proficiency with Databricks: Spark, Lakeflow Declarative Pipelines, Delta Lake, Lakebase/Postgres, Unity Catalog, Model Serving; plus experience with the Azure data ecosystem
• Experience with MLflow (or equivalent) for experiment tracking, model registry, tracing, and reproducibility
• At least one year building agentic systems that reached production use (or equivalent demonstrated work)
• Ability to mentor engineers at every level and raise team technical standards
• Familiarity with modern ML frameworks and strong judgment for reusable, scalable engineering patterns

Technologies

• Python, SQL
• Databricks, Spark, Lakeflow Declarative Pipelines, Delta Lake, Lakebase/Postgres, Unity Catalog, Model Serving
• Azure data ecosystem
• MLflow
• CI/CD
• REST endpoints
• Azure DevOps
• Databricks Declarative Automation Bundles (DABs)
• GitHub Actions
• Terraform
• Model Context Protocol (MCP)

Benefits

• Health, vision, and dental insurance
• Flexible spending accounts
• Health savings accounts
• Retirement savings plans
• Life and disability insurance programs
• Paid and unpaid time away from work
• Competitive pay (varies by eligibility factors such as geographic location, date of hire, total hours worked, job type, business line, and applicability of collective bargaining agreements)
• The company will not pay less than minimum wage for this role

Preferred Skills, Knowledge & Experience

• Experience establishing an ML or AI platform capability where none previously existed
• Experience embedding across multiple teams, geographies, and time zones as a principal-level technical contributor
• Familiarity with CI/CD and infrastructure-as-code tooling for data pipelines using Azure DevOps, Databricks Declarative Automation Bundles (DABs), GitHub Actions, Terraform, or equivalent
• Good understanding of data governance, metadata management, and cataloguing in enterprise environments
• Exposure to model serving and inference at scale (containers, REST endpoints, latency and throughput targets) or to feature stores, retrieval pipelines, or LLM cost-attribution tooling
• Model Context Protocol (MCP) or comparable tool-integration standards
• Commercial real estate, professional services, or similar domains where data is messy, entities are inconsistently defined, and subject matter experts act as the source of truth