Position Overview:We are looking for a highly skilled Senior Machine Learning Engineer with a customer-centric mindset and extensive experience in building and maintaining machine learning infrastructure on Google Cloud Platform (GCP). In this role, you will drive the design and deployment of scalable machine learning solutions, focusing on delivering a seamless experience for data scientists through a self-service platform. This position requires a strong understanding of ML pipelines, cloud infrastructure, and orchestration, with a commitment to reducing friction in data workflows and supporting model experimentation and deployment. Shyftlabs is a growing data product company founded in early 2020 and works primarily with Fortune 500 companies. We deliver digital solutions built to help accelerate the growth of businesses in various industries, by focusing on creating value through innovation.
Job Responsibilities:
Design, build, and maintain highly scalable, robust, and efficient cloud infrastructure using Google Cloud Platform (GCP) services, including Vertex AI, BigTable, BigQuery, and Cloud Composer.
Develop automation and orchestration of ML pipelines, integrating data ingestion, feature engineering, training, and deployment processes.
Collaborate with cross-functional teams to understand their needs and build solutions that improve platform usability, scalability, and the overall development experience.
Optimize data processing pipelines and cloud resources to ensure low-latency, cost-effective operation.
Implement monitoring, alerting, and failover strategies to ensure platform reliability.
Stay updated with industry trends and best practices in cloud engineering, data engineering, and machine learning
Customer-centric mindset: Passionate about delivering an exceptional experience for data scientists through a self-service platform, reducing friction in their workflows.
Collaboration: Strong communication skills to work closely with cross-functional teams, including data scientists and engineers, to ensure platform features meet user needs and expectations.
Problem-solving: Ability to identify and solve complex technical issues related to ML pipelines, cloud infrastructure, and scalability, ensuring an efficient and robust platform.
Automation-first approach: Commitment to streamlining and automating processes for scalability and reliability, enabling data scientists to focus on experimentation and model development.
Adaptability: Ability to quickly adjust to new technologies and evolving platform needs to keep the infrastructure cutting-edge and efficient.
Ownership and initiative: Comfortable taking ownership of key platform components, driving innovation and improvements that benefit the platform’s scalability and usability.
Basic Qualifications:
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
4+ years of experience in machine learning engineering or software engineering with a focus on ML infrastructure.
Hands-on experience with Google Cloud Platform services such as Vertex AI, BigTable, BigQuery, Cloud Composer, Cloud Storage, etc.
Proficiency in one or more programming languages such as Python, Java, and SQL
Experience with orchestration tools such as Apache Airflow (Composer).
Knowledge of CI/CD pipelines and DevOps tools for continuous integration and deployment.
Familiarity with containerization and orchestration (Docker, Kubernetes).Strong problem-solving skills and attention to detail.
Excellent communication skills and ability to work in a collaborative, fast-paced environment
We are proud to offer a competitive salary alongside a strong insurance package. We pride ourselves on the growth of our employees, offering extensive learning and development resources.
