Machine Learning Engineering

Company Description

Hi there! We’re Razorfish. We’ve been leading the marketing industry with our digital expertise since the start of the internet. But in 2020, we did a full reboot. What’s different? It all starts with people. Weird, wonderful, complex people - with diverse backgrounds in strategy, creative and technology. But no matter how different we are, we all have one thing in common.

We believe our differences are our strength. So we push for inclusion, challenge convention and bring in new perspectives, to inspire new ideas. Because when we connect by understanding what makes people different, we can create unforgettable experiences that enrich lives. Join us at

We’re seeking a Machine Learning Engineer to help design, build, and maintain production-grade ML systems across cloud platforms. This role blends software engineering and ML expertise to translate prototypes into scalable solutions. You’ll own the full ML lifecycle from development and deployment to monitoring and optimization using tools like Databricks, Vertex AI, and other cloud-native platforms. Strong technical skills, collaboration, and a passion for delivering AI at scale are essential.

For this role, we expect the candidate to demonstrate a track record of:

• Collaborating with Data Science teams to deploy ML solutions into production.
• Hands‑on MLOps experience, including model deployment, monitoring, and lifecycle management.
• Designing data warehouses and orchestrating data pipelines to support scalable ML operations.

• Design, build, and maintain scalable ML pipelines using cloud services (e.g., Vertex AI, Databricks, Sage Maker, Azure ML).
• Develop and integrate microservices, REST APIs, and webhooks for ML model serving.
• Implement CI/CD pipelines for automated model training, testing, and deployment.
• Create robust data processing workflows for model training and inference.

• Build and maintain ML infrastructure using modern MLOps practices and tools (e.g., MLflow, Kubeflow, Vertex AI Pipelines).
• Implement model monitoring, versioning, and performance tracking systems.
• Design automated retraining pipelines and manage model lifecycle.
• Ensure reliability, scalability, and security of models in production.
• Optimize inference performance and cost efficiency across cloud platforms.

• Write clean, maintainable, and well‑documented code following best practices.
• Implement comprehensive testing strategies including unit, integration, and model testing.
• Contribute to technical design reviews and architecture decisions.
• Maintain high code quality standards and participate in code reviews.

• Partner with data scientists to product ionize research models and prototypes.
• Collaborate with data engineers to design efficient data pipelines and feature stores.
• Work with product teams to integrate ML capabilities into customer‑facing applications.
• Participate in agile development processes and cross‑functional project planning.
• Provide technical guidance and mentorship to junior team members.

• Bachelor’s degree in Computer Science, Software Engineering, Data Science, Mathematics, or related field.
• 3‑4 years of professional experience in ML engineering, software engineering, or data science.
• 2+ years of hands‑on experience deploying and maintaining ML models in production.
• Experience working in collaborative, cross‑functional team environments.

• Programming
  
  Languages:
  
  Strong proficiency in Python and SQL.
• ML Frameworks:
  Experience with XGBoost, Tensor Flow, PyTorch, sklearn, or Keras.
• Cloud Platforms:
  Solid hands‑on experience with GCP, AWS, or Azure.
• ML Platforms:
  Practical knowledge of Vertex AI, Sage Maker, Azure ML, or Databricks.
• Analytics & Feature Engineering:
  Proficient with Big Query, Redshift, Azure Synapse.
• Distributed Processing:
  Skilled in Databricks, Apache Spark, Dataflow, Pub/Sub, Kafka.
• Workflow Orchestration:
  Experience with Airflow, Cloud Composer, Jenkins.
• Networking & Security:
  Understanding of cloud networking, security, and cost optimization.
• MLOps & Dev Ops:
  Famili…