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 razorfish.com.

Overview

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. Responsibilities ML System Development & Deployment Design, build, and maintain scalable ML pipelines using cloud services (e.g., Vertex AI, Databricks, SageMaker, 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. MLOps & Infrastructure 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. Software Engineering Excellence 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. Cross‑Functional Collaboration Partner with data scientists to productionize 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. Qualifications Education & Experience 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. Technical Skills Programming Languages: Strong proficiency in Python and SQL. ML Frameworks: Experience with XGBoost, TensorFlow, PyTorch, sklearn, or Keras. Cloud Platforms: Solid hands‑on experience with GCP, AWS, or Azure. ML Platforms: Practical knowledge of Vertex AI, SageMaker, Azure ML, or Databricks. Analytics & Feature Engineering: Proficient with BigQuery, 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 & DevOps: Familiarity with CI/CD, ML lifecycle management. API Development: Experience with REST APIs and microservices. Version Control: Proficiency with Git and collaborative development workflows. Core Competencies Strong understanding of ML algorithms, model evaluation, and validation. Experience with data preprocessing, feature engineering, and performance tuning. Solid software engineering fundamentals and coding best practices. Awareness of data privacy, security, and ethical AI principles. Excellent collaboration skills with technical and non‑technical stakeholders. Self‑driven learner with curiosity about emerging ML technologies. Preferred Qualifications Advanced Technical Skills MLOps Tools: MLflow, Kubeflow, Vertex AI Pipelines. Containerization: Docker; basic Kubernete