Decision Engine Engineer

We are seeking a highly skilled Senior Machine Learning / Data Engineer to build, enhance, and operate production-grade data and machine learning systems. This role sits at the intersection of data engineering, machine learning, real-time streaming, and MLOps, with a strong focus on continuously trained and automatically deployed ML solutions.

The ideal candidate has hands-on experience with Databricks, Kafka or comparable event-streaming technologies, machine learning pipelines, and automated model deployment. Experience with reinforcement learning is strongly preferred.

This role requires someone who can quickly understand an existing codebase and architecture, make meaningful enhancements independently, and take ownership of production ML/data workflows with minimal hand-holding.

Key Responsibilities

• Design, develop, and maintain scalable data engineering and machine learning workloads using Databricks.
• Build and optimize data pipelines supporting both batch and real-time ML workloads.
• Develop event-driven data and ML solutions using Kafka or comparable streaming technologies.
• Build machine learning pipelines covering data preparation, feature engineering, model training, evaluation, validation, and deployment.
• Implement continuous training and automated model deployment pipelines, ensuring models can be retrained and promoted to production without relying on one-off batch processes.
• Develop and maintain MLOps workflows for model versioning, monitoring, retraining, deployment, and rollback.
• Apply machine learning techniques to high-volume, production-scale datasets.
• Leverage reinforcement learning techniques where appropriate; hands-on RL experience is strongly preferred.
• Integrate ML models with existing data platforms, APIs, services, and event-streaming architectures.
• Monitor model performance, data quality, model drift, and pipeline reliability in production.
• Work within and extend an existing enterprise codebase and architecture without extensive hand-holding.
• Perform code reviews, write automated tests, troubleshoot production issues, and improve system reliability and scalability.
• Collaborate with data engineers, ML engineers, software engineers, architects, and product stakeholders.
• Use Claude or comparable AI coding assistants as part of day-to-day development for coding, debugging, refactoring, documentation, testing, and codebase exploration.
• Identify opportunities to improve engineering productivity through automation and AI-assisted development.

Required Qualifications

• 7+ years of experience in data engineering, machine learning engineering, software engineering, or a closely related field.
• Strong hands-on experience with Databricks for data engineering and/or machine learning workloads.
• Strong experience with Python and modern software engineering practices.
• Hands-on experience with Kafka or comparable event-streaming technologies.
• Solid understanding of machine learning concepts, model development, and production ML systems.
• Experience building automated ML training and deployment pipelines.
• Experience with continuous training, model versioning, automated deployment, and production ML lifecycle management.
• Ability to work effectively within an existing architecture and independently extend an established codebase.
• Practical, day-to-day experience using Claude, GitHub Copilot, Cursor, or comparable AI coding assistants.
• Strong understanding of CI/CD, testing, version control, and production engineering practices.
• Experience working with cloud-based data and ML platforms.

Preferred Qualifications

• Hands-on experience with reinforcement learning (RL).
• Experience with ML optimization, experimentation, model evaluation, and hyperparameter tuning.
• Experience with MLOps / LLMOps platforms and practices.
• Experience with MLflow or comparable model lifecycle management tools.
• Experience with Spark / PySpark and distributed data processing.
• Experience with Kubernetes, Docker, Terraform, or other modern infrastructure technologies.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Experience implementing real-time feature engineering and streaming ML pipelines.
• Familiarity with model monitoring, drift detection, observability, and automated retraining.

Core Technical Skills

Must Have:

• Databricks
• Python
• Machine Learning
• Kafka / Event Streaming
• MLOps
• Continuous Training
• Automated Model Deployment
• CI/CD
• Production Software Engineering
• AI Coding Assistants

Strongly Preferred:

• Reinforcement Learning
• MLflow
• Spark / PySpark
• Kubernetes / Docker
• Cloud Platforms
• Real-Time ML / Streaming
• Model Monitoring & Drift Detection