Principal Machine Learning Engineer

Lead the design and delivery of end-to-end AI and machine learning systems, spanning architecture, engineering, deployment, and production monitoring.

Responsibilities

• Shape end-to-end architecture, development, and deployment for AI and machine learning, including GenAI and agentic models tailored to business use cases.
• Design and build agentic AI systems with multi-agent orchestration, tool-use, evaluation, and autonomous task-execution pipelines for enterprise workflow automation.
• Set and enforce model development standards across data preprocessing, feature engineering, model selection, hyperparameter tuning, evaluation, and documentation.
• Partner with data engineering teams to deliver robust, scalable, high-quality data pipelines supporting both training and inference.
• Implement MLOps and LLMOps capabilities, including CI/CD 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 policies for model risk management, fairness, explainability, privacy, and security.
• Promote AI principles across the organization, ensuring human oversight and accountability throughout the AI development lifecycle.
• Work across project and product teams and mentor engineers, while establishing ML engineering and architectural principles using Databricks on Azure.

Requirements

• 10+ years building software or machine learning systems, including 6+ years shipping and operating machine learning in production (served, monitored, and maintained after launch).
• Experience as the senior-most or near-senior-most engineer on a production ML or data platform, owning architectural decisions that others depend on.
• Proven ability to set technical standards that persist beyond your involvement and to lead through influence without formal authority.
• Hands-on Python and SQL, plus software engineering fundamentals: version control, automated testing, code review, modular design, and cross-team enforcement.
• Expert, hands-on Databricks proficiency (Spark, Lakeflow Declarative Pipelines, Delta Lake, Lakebase/Postgres, Unity Catalog, Model Serving) and experience with the Azure data ecosystem.
• Experience with MLflow or an equivalent solution 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 machine learning frameworks and reusable, scalable engineering patterns.

Technologies

• Python, SQL
• Databricks, Spark
• Lakeflow Declarative Pipelines, Delta Lake, Lakebase/Postgres
• Unity Catalog, Model Serving
• Azure data ecosystem, CI/CD
• MLflow
• 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 in an organization without prior capability.
• 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 feature stores, retrieval pipelines, or LLM cost-attribution tooling.
• Model Context Protocol (MCP) or comparable tool-integration standards.
• Domain experience in commercial real estate, professional services, or similar environments where data is messy, entities are inconsistently defined, and subject matter experts are the source of truth.

Location: Creve Coeur, MO (onsite) | Salary: USD 178,500 - 210,000 per yearly