Job Description:
Role: ML Ops Engineer
Duration: Long-Term Contract
Location: Hybrid - 4 days/week onsite
Description:
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.
Key Responsibilities
ML Ops & Machine Learning
- 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
Data Engineering & Platform Development
- 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
DevOps & Platform Operations
- Manage and maintain data platform infrastructure using Terraform and CI/CD pipelines
- Enhance DevOps 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 SonarQube, Checkmarx, Fossa, and Cycode
Data Governance & Quality
- 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
Collaboration & Continuous Improvement
- 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
Required Skills
- 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
- DevOps tooling (GitHub, Tekton, Docker, Terraform, CI/CD pipelines)
- Experience implementing data governance frameworks
Preferred Skills
- 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
Required Experience
- 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., BigQuery, Redshift, Synapse)
- Workflow orchestration tools (e.g., Airflow)
- Relational databases (MySQL, PostgreSQL, SQL Server)
- Streaming platforms (Kafka, Pub/Sub)
- Microservices architectures and REST APIs
- DevOps tools (GitHub, Tekton, Terraform, Docker)
- Agile tools (Jira)
Preferred Experience
- 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
Education
- Required: Bachelor’s Degree
- Preferred: Master’s Degree or higher
