Developer Jobs in Creve Coeur, MO

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Principal Machine Learning Engineer

Cushman & Wakefield Creve Coeur, MO

Creve Coeur, MO

Today

$179k - $210k/yr Full time Ai Ml Artificial Intelligence Azure DevOps

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Job Description

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

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