About Tekion :

Positively disrupting an industry that has not seen innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers, Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting-edge technology, big data, machine learning, and AI to seamlessly bring together OEMs, retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities, Tekion is enabling the best automotive retail experiences ever. We're a global community of talented individuals, with tech innovators collaborating across North America, Asia, and Europe. This diverse and geographically distributed team brings a wealth of perspectives and experiences to Tekion, allowing us to understand our customers and build a truly world-class product.

Global presence - US, India, Canada, UK, France, Germany, Netherlands & Philippines.

Job Description :

Function : Data Science and Analysis / Data Science / Machine Learning

Machine Learning Python Kafka AWS TensorFlow

Responsibilities :

- Execute the R& D and product roadmap based on industry insights and business needs.

- Collaborate with stakeholders to align ML solutions with business objectives.

- Develop robust APIs and microservices for seamless ML model integration into production systems.

- Build feature pipelines for model serving and ensure effective integration with front-end applications, databases, and back-end services.

- Mentor and guide machine learning engineers, fostering team growth through training and collaboration.

- Conduct code reviews to maintain quality and adhere to best practices.

- Manage end-to-end MLOps pipelines for data collection, model training, validation, and monitoring.

- Ensure adherence to version control, testing, and model governance best practices.

- Implement model compression, quantization, and distributed training techniques.

- Track key metrics and optimize models post-deployment.

- Work with cloud architects and DevOps to design scalable ML infrastructure.

- Oversee deployment and management of compute and storage resources for model training and inference.

- Collaborate with applied scientists and analysts to convert model requirements into production-ready solutions.

- Establish monitoring and alerting systems for deployed models to ensure prompt issue resolution.

- Create and maintain documentation for ML architecture and best practices.

- Stay current with ML technologies and contribute to ongoing enhancement efforts.

Requirements :

- Bachelor's/Master's / PhD in Computer Science or related field.

- 6+ years of hands-on experience as a Machine Learning Engineer or Architect with a strong portfolio of deployed ML models for batch, streaming, and real-time use cases.

- Proficient in Python for model development and data manipulation, and experience with Java or Scala for building production systems.

- Familiarity with messaging queues (e. g., Kafka, SQS) and MLOps tools (e. g., MLflow, Kubeflow, Airflow).

- Experience with cloud platforms (AWS, Google Cloud, Azure) and containerization (Docker, Kubernetes).

- Knowledge of machine learning frameworks (e. g., TensorFlow, PyTorch) and data stores (e. g., Elasticsearch, MongoDB, PostgreSQL).

- Knowledge of data processing and ETL tools (e. g., Apache Spark, Kafka).

- Experience in monitoring tools Grafana and Prometheus.

- Strong problem-solving skills and analytical mindset.

- Experience in large-scale production systems and distributed computing.

- Contributions to open-source projects or active participation in the ML community.

- Demonstrated leadership capabilities and experience in mentoring junior engineers.

- Innovative mindset with a track record of developing solutions leading to significant business improvements or patents.

- A collaborative approach to working across multiple products and application teams.

- A willingness to learn, share, and improve continuously.