Machine Learning Engineer

Job summary

Design and maintain Databricks-based ML workflows and infrastructure for experiment tracking, feature management, model training, deployment, and lifecycle operations. Build CI/CD and monitoring systems that support reliable model serving, detect drift or performance issues, and trigger retraining when needed.

Job details

At Lumenalta, we partner with forward-thinking organizations to build technology solutions that scale, delight users, and accelerate business growth. Our global teams bring curiosity, commitment, and technical excellence to every project. We value transparency, autonomy, and impact—empowering every team member to do their best work. We’re seeking an experienced MLOps Engineer responsible for operationalizing machine learning at scale on the Databricks platform. This role bridges data engineering and ML, building the infrastructure and workflows that take models from experimentation to reliable production deployments. What You'll Be Doing Design and maintain MLflow-based workflows for experiment tracking, model registry, versioning, and lifecycle management. Build and manage Feature Store infrastructure to enable reusable, consistent feature pipelines across teams and use cases. Develop model deployment pipelines, including serving infrastructure, A/B testing support, versioning, and rollback strategies. Implement CI/CD pipelines tailored for ML workflows, including automated testing, validation gates, and deployment triggers. Orchestrate distributed model training on Databricks, optimizing for compute efficiency, reproducibility, and cost. Monitor deployed models for data drift, performance degradation, and system health, triggering automated retraining workflows as needed. Collaborate with Data Scientists and Data Engineers to reduce friction between experimentation environments and production. What We're Looking For 3–5+ years in MLOps, ML platform engineering, or DevOps for ML, with proven production ML deployments. Hands-on expertise with MLflow for tracking, registry, and project management within Databricks or standalone environments. Experience building and consuming Feature Store solutions (Databricks Feature Store or equivalent). Proven experience deploying and serving ML models at scale, including real-time and batch inference patterns. Ability to design automated pipelines for model training, validation, and deployment using modern CI/CD tooling. Strong familiarity with Databricks for distributed training, job orchestration, and cluster management. Knowledge of model monitoring practices, including drift detection, alerting, and retraining triggers. Why Lumenalta is an amazing place to work at At Lumenalta, you can expect that you will: Be 100% dedicated to one project at a time so that you can innovate and grow. Be a part of a team of talented and friendly senior-level developers. Work on projects that allow you to use leading tech. Salary Salary range: CA$110,000 - CA$192,000 annually, with final compensation determined by your qualifications, expertise, experience, and the role's scope. Location: This is a fully remote position; however, candidates must be based in regions that align with the Pacific, Central, or Eastern U.S. time zones to ensure effective collaboration with client and team schedules. Benefits In addition to competitive pay, we offer a variety of benefits to support your professional and personal growth, including: Flexible working hours in a remote environment. Health insurance (medical and dental) for T4 Employees. A professional development fund to enhance your skills and knowledge. 15 days of paid time off annually. Access to soft-skill development courses to further your career. Position Details This is a full-time position requiring a minimum of 40 hours per week, Monday through Friday. Application Deadline Applications will be accepted until November 22nd, 2026. Candidates can expect feedback by November 30th, 2026.

What you’ll do

Design and maintain Databricks-based ML workflows and infrastructure for experiment tracking, feature management, model training, deployment, and lifecycle operations. Build CI/CD and monitoring systems that support reliable model serving, detect drift or performance issues, and trigger retraining when needed.

Requirements

Candidates need 3–5+ years of experience in MLOps, ML platform engineering, or ML-focused DevOps, with a record of production ML deployments. They should have hands-on experience with MLflow, Feature Stores, Databricks, scalable model serving, CI/CD automation, distributed training, and model monitoring.

Benefits

• Flexible Working Hours
• Health Insurance
• Dental Insurance
• Professional Development Fund
• 15 Days of Paid Time Off
• Soft-Skill Development Courses

Listed skills

• CI/CD · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• MLOps
• MLflow
• Databricks
• Feature Store
• Machine Learning Model Deployment
• Model Serving
• CI/CD
• Distributed Model Training
• Job Orchestration
• Cluster Management
• Model Monitoring
• Data Drift Detection
• Automated Retraining
• A/B Testing
• Batch Inference
• Real-Time Inference

Job areas

• Technology
• Software
• Data & Analytics
• Engineering
• Consulting

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