Senior Machine Learning​/MLOps Engineer

Senior Machine Learning / MLOps Engineer Overview / Summary We are seeking a Senior Machine Learning / MLOps Engineer to build scalable and robust machine learning data pipelines in the cloud and support AI-driven initiatives. This role will focus on ML operations, cloud data engineering, data governance, production data pipelines, infrastructure, and continuous optimization of data and ML solutions. The role requires strong technical communication skills and the ability to collaborate with data analytics stakeholders, cross-functional teams, and management.

Key Responsibilities Build scalable and robust ML data pipelines in the cloud to process large volumes of connected vehicle data. Optimize existing ML solutions for performance, security, and cost-effectiveness. Utilize continual learning methods to continuously improve model performance. Develop analytical data products using streaming and batch ingestion patterns on Google Cloud Platform. Build data pipelines to monitor data quality and the performance of analytical models and AI solutions.

Maintain data platform infrastructure using Terraform. Continuously develop, evaluate, and deliver code using CI/CD practices. Collaborate with data analytics stakeholders to streamline data acquisition, processing, and presentation. Implement an enterprise data governance model focused on data protection, sharing, reuse, quality, and standards. Enhance and maintain Dev Ops capabilities of the data platform. Optimize existing data solutions, including pipelines, products, and infrastructure, for performance, security, reliability, and cost.

Work in an Agile product team using Test Driven Development (TDD), continuous integration, and continuous deployment (CI/CD). Address code quality issues using Sonar Qube, Checkmarx, Fossa, and Cycode throughout the development lifecycle. Perform data mapping and data lineage activities and document information flows. Monitor production pipelines and provide production support by addressing production issues according to SLAs. Analyze connected vehicle data to support new product development and production vehicle improvements.

Identify data quality, vehicle, and feature issues and work with business owners to resolve them. Communicate technical concepts clearly and advocate for well-designed solutions. Continuously enhance domain knowledge of connected vehicle data, connected services, and algorithms, models, and solutions developed by data scientists and AI engineers. Stay current with data engineering practices and contribute to technical direction while maintaining a customer-centric approach.

Mentor and advise junior team members to expand ML Ops expertise across the organization.

Required Qualifications Bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Engineering, or a related field. Master's degree or foreign equivalent in a related field with 4 years of experience, or an equivalent combination of education and experience, including 6+ years with a bachelor's degree. At least 4 years of professional experience in data engineering, data product development, and software product launches.

At least 4 years of experience with at least three of the following:
Java, Python, Spark, Scala, SQL. At least 3 years of cloud data/software engineering experience building scalable, reliable, and cost-effective production batch and streaming data pipelines. Deep knowledge of implementing ML/AI Ops on Google Cloud Platform.

Experience with Machine Learning and MLOps.

Experience with Python. Knowledge of Tensor Flow.

Experience with data governance. Knowledge of Artificial Intelligence and Expert Systems.

Experience with Git Hub.

Experience with cloud data warehouses such as Google Big Query, Amazon Redshift, or Microsoft Azure Synapse Analytics.

Experience with workflow orchestration tools such as Airflow.

Experience with relational database management systems such as MySQL, PostgreSQL, or SQL Server.

Experience with real-time data streaming platforms such as Apache Kafka or GCP Pub/Sub.

Experience with microservices architecture for large-scale real-time data processing applications.

Experience with REST APIs for compute, storage, operations, and security.

Experience with Dev Ops tools such as Tekton, Git Hub Actions, Git, Git Hub, Terraform, and Docker.

Experience with project management tools such as Atlassian JIRA. Strong technical communication and collaboration skills. Ability to work in an Agile environment. Ability to troubleshoot data pipeline and product issues. Ability to simplify and clearly communicate complex data and software concepts. Ability to work with cross-functional teams and all levels of management independently.

Preferred Qualifications Ph.D. or foreign equivalent degree in Computer Science, Software Engineering, Information Systems, Data Engineering, or a related field. 2 years of experience with ML model development and/or MLOps. Experience contributing code to open-source…