Positively disrupting an industry that has not seen any 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. Tekion employs close to 3,000 people across North America, Asia and Europe.
Location: Bangalore Key Responsibilities
Lead the execution of the RD and product roadmap, leveraging industry insights and business needs to drive ML initiatives while managing team priorities and timelines.
Collaborate with cross-functional stakeholders to ensure alignment of ML solutions with overarching business objectives, effectively communicating technical concepts to non-technical audiences.
Oversee the development of robust APIs and microservices, ensuring smooth integration of ML models into production environments, and guide the team in building feature pipelines for model serving.
Mentor and develop machine learning engineers, fostering a positive and productive work environment through training, guidance, and constructive feedback.
Conduct code reviews and establish best practices to maintain high quality and performance standards while promoting adherence to version control and model governance.
Manage and optimize end-to-end MLOps pipelines for data collection, model training, validation, and monitoring, while ensuring team collaboration and effective resource allocation.
Drive the implementation of model compression, quantization, and distributed training techniques to enhance performance, encouraging innovative solutions from team members.
Track key metrics and optimize deployed models to ensure ongoing effectiveness, collaborating with team members to identify improvement opportunities.
Collaborate with cloud architects and DevOps teams to design and maintain scalable ML infrastructure, ensuring effective resource management and deployment.
Work closely with applied scientists and analysts to transform model requirements into production-ready solutions, facilitating teamwork across departments.
Establish and maintain monitoring and alerting systems for deployed models, ensuring prompt issue resolution while guiding the team in best practices.
Create and uphold documentation for ML architecture and best practices to ensure knowledge sharing within the team, promoting continuous improvement.
Stay current with advancements in ML technologies and lead ongoing enhancement initiatives within the team, encouraging team participation in the ML community.
Required Qualifications
Bachelors/ Masters / PhD in Computer Science or related field.
9+ years of experience in machine learning, with a strong portfolio of deployed ML models for various use cases, including batch, streaming, and real-time.
3+ years of experience in people management, leading teams of 7 or more members
Proficient in Python for model development and data manipulation, with experience in 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 technologies (Docker, Kubernetes).
Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and databases (e.g., Elasticsearch, MongoDB, PostgreSQL).
Understanding of data processing and ETL tools (e.g., Apache Spark, Kafka).
Experience with monitoring tools like Grafana and Prometheus.
Strong problem-solving skills and an analytical mindset.
Preferred Qualifications
Experience managing large-scale production systems and distributed computing environments.
Demonstrated leadership capabilities with experience mentoring and developing junior engineers, along with strong project management skills.
An innovative mindset with a track record of developing solutions that yield significant business improvements or patents.
A collaborative approach to working across multiple product and application teams, with excellent communication and conflict resolution skills.
A commitment to continuous learning, sharing knowledge, and improving team practices.
Perks and Benefits
Competitive compensation
Generous stock options
Medical Insurance coverage
Work with some of the brightest minds from Silicon Valley s most dominant and successful companies