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Senior AI Solutions Engineer
3 months ago
About Charger Logistics Inc.
Charger Logistics Inc. stands as a premier asset-based carrier with a robust presence across North America. With over two decades of expertise in delivering exceptional logistics solutions, we have evolved into a leading transport provider and continue to expand our reach.
At Charger Logistics, we prioritize the growth and development of our employees, fostering an environment that encourages learning and professional advancement. Our entrepreneurial spirit embraces individual ideas and innovative strategies, making us a dynamic organization. We are currently seeking a driven individual to enhance our team.
Key Responsibilities:
- Develop engaging experiences utilizing language models to interpret and respond to customer inquiries.
- Analyze customer data to construct ranking models that facilitate a tailored search experience.
- Participate in all stages of the machine learning lifecycle, including data collection, annotation, modeling, evaluation, deployment, and monitoring.
- Implement vector embedding search and information retrieval methods to access pertinent data.
- Optimize systems to manage extensive search volumes efficiently and maintain high performance.
- Balance cost and performance considerations to enhance operational efficiency.
- Collaborate with applied scientists and engineers to select appropriate models and tools for projects.
- Communicate insights effectively to both technical and non-technical stakeholders.
- Lead scientific projects, coordinating deliverables, drafting papers, modeling, and creating research plans.
- Stay updated on advancements in the LLM and conversational systems domain, experimenting and integrating the most suitable tools.
Qualifications:
- Over 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 platforms like Hive, Hadoop, and Spark.
- Strong SQL capabilities and familiarity with data manipulation libraries such as 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 tools for machine learning, including 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 for specific domain challenges.
- Prior experience in responsible AI development, focusing on ethical and fairness considerations in model tuning.
