Senior AI Engineer

1 month ago

Brampton, Ontario, Canada Charger Logistics Full time

Company Overview

Charger Logistics Inc. stands as a premier asset-based carrier with a robust presence across North America. With two decades of expertise in delivering top-tier logistics solutions, Charger Logistics has evolved into a leading transport provider and continues to expand its operations.

At Charger Logistics, we prioritize the development of our employees, offering them the opportunity to enhance their skills and advance their careers. We foster an entrepreneurial spirit, encouraging innovative ideas and strategies from our team members. We are currently seeking a driven individual to enhance our operations.

Key Responsibilities

  • Design and implement engaging experiences utilizing language models to interpret and address customer inquiries.
  • Analyze customer data to develop ranking models that facilitate a tailored search experience.
  • Participate in all phases of the machine learning lifecycle: data gathering, annotation, modeling, evaluation, deployment, and monitoring.
  • Employ vector embedding search and information retrieval methodologies to extract pertinent data.
  • Optimize systems to manage up to 1 billion searches daily while ensuring high performance.
  • Balance cost and performance considerations to enhance operational efficiency.
  • Collaborate with applied scientists and engineers to select appropriate models and tools for architecture.
  • Communicate effectively with both technical and non-technical stakeholders, sharing insights and knowledge.
  • Act as the primary contact for scientific projects, overseeing deliverables, drafting papers, modeling, and creating scientific plans.
  • Stay informed about the latest developments in the LLM and conversational systems, experimenting and iterating to integrate the best tools.

Qualifications

  • A minimum of 5 years of practical experience in developing ML/AI systems for advanced information retrieval, ranking, or recommendation systems.
  • Proficiency in machine learning frameworks and libraries such as Scikit-learn, SciPy, TensorFlow, and Keras.
  • Experience with large-scale distributed data processing technologies including Hive, Hadoop, and Spark.
  • Strong SQL capabilities and familiarity with data manipulation libraries like Pandas and NumPy.
  • Understanding of various Transformer-based architectures and their applications.
  • Expertise in deploying scalable, mission-critical ML models in production environments.
  • Knowledge of Kubernetes-based machine learning tools such as Kubeflow and KServe.
  • Experience with monitoring and logging solutions compatible with Kubernetes.
  • Familiarity with MLOps best practices and management of the production ML lifecycle.
  • Proficient in developing fine-tuned language models to address domain-specific challenges.
  • Prior experience in responsible AI development, focusing on ethical and fairness considerations in model tuning.

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