Machine Learning Architect, Tekion

• The successful candidate will be a self-starter, comfortable with ambiguity, have an affinity for building from scratch, demonstrate strong attention to detail, and have the ability to work in a fast-paced, complex and ever-changing environment
• Bachelor’s/Master’s in Computer Science or a related field
• 7–10 years of hands-on experience as a Machine Learning Engineer or in a similar role, with a strong portfolio of deployed ML models
• Strong problem-solving and analytical skills
• Proficient in Python for model development and data manipulation
• Familiarity with Java or Scala for production systems and microservices
• Experience with messaging queues like Kafka or SQS
• Familiarity with MLOps tools and frameworks (e.g., MLflow, Kubeflow, Airflow)
• Working knowledge of cloud platforms (AWS, Google Cloud, Azure) and containerization (Docker, Kubernetes)
• Basic understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn)
• Familiarity with data stores like Elasticsearch, Postgres, MongoDB, or Redis
• Experience with data processing tools (e.g., Apache Spark, Kafka)
• Familiarity with monitoring tools such as Grafana or Prometheus

Desirable

• The Machine Learning Architect will help shape the future of Automotive Industry.
• At Tekion we transform the end to end experience of customers in automotive. Are you motivated by solving tough problems, business-focused and have a passion for data driven decisions?
• You’ll be able to bring your unique skills and expertise to shape the direction of our Data Science and Machine Learning team. You will have an opportunity to work with state-of-the-art machine learning algorithms on large datasets
• Serve as a liaison between R&D and stakeholders to ensure technical alignment with business objectives
• Contribute to the development, scaling, and optimization of ML/AI solutions in alignment with company goals
• Support shaping the R&D roadmap by collaborating with stakeholders to understand business needs
• Develop APIs and microservices to enable seamless integration of ML models into production systems
• Collaborate on designing and building feature pipelines for ML models
• Ensure integration of ML models with front-end applications, databases, and back-end services
• Collaborate with the team to publish findings and advance field knowledge in key areas
• Work with machine learning engineers and cross-functional teams to support team growth and collaboration
• Assist in building and maintaining MLOps pipelines for data collection, model training, validation, and monitoring
• Implement version control, testing, and model governance practices as part of the ML lifecycle
• Identify and address basic bottlenecks in ML models and services, with guidance from senior staff
• Apply techniques like model optimization and distributed training under mentorship
• Track metrics and support post-deployment optimization efforts
• Collaborate with cloud architects and DevOps teams to design scalable ML infrastructure
• Assist in the deployment and management of compute and storage resources for ML workflows
• Partner with applied scientists and analysts to translate model requirements into production-ready solutions
• Contribute to establishing monitoring and alerting systems for deployed models
• Assist in creating and maintaining documentation for ML architecture and best practices
• Stay informed about ML technologies and suggest relevant enhancements