Principal Machine Learning Engineer

Principal Machine Learning Engineer role for onsite work in Creve Coeur, MO, focused on end-to-end AI/ML architecture, MLOps/LLMOps, and production monitoring.

• Build and maintain AI/ML systems across the full lifecycle: end-to-end architecture, development, deployment, and ongoing optimization
• Shape architecture for AI use cases including machine learning, GenAI, and agentic models tailored to business requirements
• Design agentic AI systems with multi-agent orchestration, tool-use, evaluation, and autonomous task-execution pipelines
• Set and enforce ML development standards covering data preprocessing, feature engineering, model selection, hyperparameter tuning, evaluation, and documentation
• Partner with data engineering to deliver robust, scalable, high-quality data pipelines for both training and inference
• Implement MLOps and LLMOps including CI/CD pipelines for data wrangling, feature engineering, model training, evaluation, deployment, and monitoring
• Define model operations standards for versioning, experiment tracking, reproducibility, and model registry management
• Operate production monitoring to detect performance degradation, data drift, concept drift, and bias, including automated alerting and retraining triggers
• Support AI governance with policies for model risk management, fairness, explainability, privacy, and security
• Embed human oversight and accountability across the AI development lifecycle

Requirements

• 10+ years building software or machine learning systems, including 6+ years shipping and operating ML in production (served, monitored, and maintained after launch)
• Experience acting as the senior-most or near-senior-most engineer on a production ML or data platform, accountable for architectural decisions
• Track record of establishing technical standards that continue beyond your involvement
• Ability 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 enforcement across teams
• Expert hands-on proficiency with Databricks and the Azure data ecosystem, including: Spark, Lakeflow Declarative Pipelines, Delta Lake, Lakebase/Postgres, Unity Catalog, and Model Serving
• Experience with MLflow (or equivalent) for experiment tracking, model registry, tracing, and reproducibility
• At least 1 year building agentic systems that reached production use (or equivalent demonstrated work)
• Proven 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
• 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/AI platform capability in an organization that did not previously have one
• Experience embedding across multiple teams, geographies, and time zones as a principal-level 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 exposure such as commercial real estate or professional services where data is messy, entities are inconsistently defined, and subject matter experts are the source of truth