ML Ops Engineer

ML Ops Engineer

This role focuses on building and scaling enterprise-grade ML Ops and data engineering solutions to support connected vehicle data and agentic AI initiatives. The individual will design, implement, and optimize data pipelines and machine learning operations in a cloud-native environment, ensuring performance, reliability, and governance at scale.

• Build scalable, secure, and high-performance ML data pipelines in the cloud to process large volumes of connected vehicle data
• Support and evolve ML/AI solutions, including agentic systems, with a focus on performance optimization, cost efficiency, and security
• Implement continuous learning frameworks to improve model accuracy and performance over time
• Design and develop data products leveraging both streaming and batch ingestion patterns on Google Cloud Platform
• Build and maintain data pipelines to monitor: data quality, model performance, agentic solution effectiveness
• Support real-time and large-scale data processing using modern data engineering practices
• Manage and maintain data platform infrastructure using Terraform and CI/CD pipelines
• Enhance Dev Ops capabilities, including continuous integration, deployment, and automation
• Monitor production pipelines and provide support in accordance with SLAs
• Identify and resolve code quality and security issues using tools such as Sonar Qube, Checkmarx, Fossa, and Cycode
• Implement and promote enterprise data governance practices, including data protection, standardization, quality, and reuse
• Perform data mapping, lineage tracking, and documentation of data flows
• Provide visibility into data quality, vehicle, and feature-level issues and partner with stakeholders to resolve
• Collaborate with cross-functional teams to streamline data acquisition, processing, and analytics delivery
• Support business and product teams with insights derived from connected vehicle data
• Stay current with emerging data engineering and ML Ops practices and contribute to the technical direction of the organization
• Mentor junior team members and promote best practices across the team

• Strong communication skills with the ability to translate complex ML/AI concepts to both technical and non-technical audiences
• Deep expertise in Google Cloud Platform (GCP)
• Strong experience in ML Ops, Machine Learning, and AI systems
• Proficiency in Python and familiarity with Java, Spark, and SQL
• Experience building scalable data pipelines and microservices architectures
• Knowledge of Apache Kafka or real-time streaming platforms, REST APIs for system integration, Dev Ops tooling (Git Hub, Tekton, Docker, Terraform, CI/CD pipelines)
• Experience implementing data governance frameworks

• Experience with connected vehicle data or telematics
• Data modeling and database design expertise
• Experience with cloud infrastructure and distributed systems
• Data mining and advanced analytics experience
• Strong troubleshooting and problem-solving skills
• Experience mentoring or supporting junior team members

• Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or related field, plus 6+ years of experience (or equivalent combination)
• 4+ years of experience in: data engineering and data product development, software development and production system delivery
• Experience with at least three of the following:
  Java, Python, Spark, Scala, SQL
• 3+ years of experience building scalable cloud-based data pipelines using: data warehousing solutions (e.g., Big Query, Redshift, Synapse), workflow orchestration tools (e.g., Airflow), relational databases (MySQL, PostgreSQL, SQL Server), streaming platforms (Kafka, Pub/Sub), microservices architectures and REST APIs, Dev Ops tools (Git Hub, Tekton, Terraform, Docker), Agile tools (Jira)

• Master's or PhD in a related field
• Hands-on experience with ML model development and/or ML Ops
• Experience contributing to open-source projects
• Experience with cloud architecture design and migrations
• GCP certifications
• Proven ability to: automate complex data pipelines, troubleshoot and optimize data platforms, communicate complex technical concepts clearly, deliver end-to-end solutions from design to production

• Required:
  
  Bachelor's Degree
• Preferred:
  Master's Degree or higher