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
EEO Employer
Apex Systems is an equal opportunity employer. We do not discriminate or allow discrimination on the basis of race, color, religion, creed, sex (including pregnancy, childbirth, breastfeeding, or related medical conditions), age, sexual orientation, gender identity, national origin, ancestry, citizenship, genetic information, registered domestic partner status, marital status, disability, status as a crime victim, protected veteran status, political affiliation, union membership, or any other characteristic protected by law. Apex will consider qualified applicants with criminal histories in a manner consistent with the requirements of applicable law.
UnitedHealthcare creates and publishes the Transparency in Coverage Machine-Readable Files on behalf of Everforth Apex Systems.
VEVRAA Federal Contractor.
We request Priority Protected Veteran & Disabled Referrals for all of our locations within the state.
