HTC Global Services is looking for an experienced Machine Learning Engineer to design, implement, and maintain robust analytics pipeline solutions. These solutions will support the analysis, modeling, and prediction of upstream and downstream auction prices, directly benefiting the Business and Sales Planning Analytics (BSPA) Used Vehicle Analytics team and its customers.
Requirements
- GCP Cloud Run, Kubernetes, Spark, SonarQube, GCP, Google Cloud Platform, Tekton, Python, API, Jira, AWS
- Experience with version control systems like GitHub for managing code repositories and collaboration.
- Experience with code quality and security scanning tools, such as, SonarQube, Cycode and FOSSA.
- Experience with data engineering tools and technologies, such as, Kubernetes, Container-as-a-Service (CaaS) platforms, OpenShift, DataProc, Spark (with PySpark) or Airflow.
- Experience with CI/CD practices and tools, including Tekton or Terraform, as well as containerization technologies like Docker or Kubernetes.
- Familiarity with cloud computing platforms like AWS, Azure, or Google Cloud Platform.
- Familiarity with Atlassian project management tools (e.g., Jira, Confluence) and agile practices.
Responsibilities
- Designing, building, deploying and scaling complex self-running ML solutions in areas like computer vision, perception, localization etc.
- Automate and optimize the end-to-end ML model lifecycle using their expertise in experimental methodologies, statistics, and coding for tool building and analysis.
- Design and develop innovative ML models and software algorithms to solve complex business problems in both structured and unstructured environments
- Design, build, maintain and optimize scalable ML pipelines, architecture and infrastructure
- Deploy ML models and algorithms into production and run simulations for algorithm development and test various scenarios
- Automate model deployment, training and re-training, leveraging principles of agile methodology, CI/CD/CT (Continuous Integration/ Continuous Deployment/ Continuous Training) and MLOps
- Enable model management for model versioning and traceability to ensure modularity and symmetry across environments and models for ML systems
Other
- Collaborate with business and technology stakeholders to understand current and future ML requirements
- Collaborate with development and operations teams to implement software solutions that improve system integration and automation of ML pipelines.
- Collaborate with technical and non-technical teams to gather integration requirements and ensure successful deployment of data solutions.
- Work with IT to ensure systems meet evolving business needs and comply with data governance policies and security requirements.
- Manage deliverables through project management tools.
