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Engineer, Technology Deployment

1 week ago

Vancouver, Canada Alquemy Search & Consulting Full time
Alquemy's client is a leading organization in the higher education sector. They are seeking an AI Engineer for a One Year Term (contract) Salaried position.
The AI Engineer is responsible for designing, developing, deploying, and monitoring advanced AI and ML models to address business challenges. This role focuses on end-to-end machine learning solutions, including intelligent chatbot applications, MLOps best practices, and scalable AI model deployment on cloud platforms. The successful candidate will work with technologies like AWS SageMaker, Amazon Bedrock, MLflow, Apache Tika, and various MLOps frameworks to ensure robust model versioning, monitoring, and lifecycle management. Hugging Face, LangChain, spaCy) and work closely with the Project Manager and AI Data Engineer to develop efficient AI-driven solutions.

Develop and maintain MLOps pipelines to automate model training, validation, and deployment.
Ensure proper model versioning and reproducibility using MLflow, SageMaker Pipelines, or Kubeflow.
Build and maintain intelligent chatbot solutions, employing techniques such as: Improve response accuracy with relevant contextual data.
Utilize Apache Tika for extracting and processing unstructured data (PDFs, text, documents, images).
Clean, process, and transform raw datasets for efficient model training and inference.
Bachelor's or Master’s degree in Computer Science, Data Science, AI, Machine Learning, or a related field.
~5–8 years of experience in AI/ML development, machine learning engineering, or software engineering roles.
~ Strong understanding of ML models, fine-tuning, supervised learning, and deep learning techniques.
~ Experience deploying chatbot solutions using RAG, prompt engineering, and knowledge graphs.
~ Proficiency in MLflow, SageMaker Pipelines, or Kubeflow for model versioning and tracking.
~ Experience with model monitoring using SageMaker Model Monitor, AWS CloudWatch, or Prometheus.
~ Strong experience in CI/CD for ML pipelines (GitHub Actions, Jenkins, or AWS CodePipeline). Our client works in a hybrid model with 3-4 days/week required on site.