AI / ML Engineer
New
CanadaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 7+ years
- Required Skills
- AWSPythonGCPMachine LearningAzureCI/CDMLOpsGenerative AILangChain
Requirements
- 7+ years of professional experience in machine learning engineering, software engineering, or data science roles focused on building production-grade ML systems.
- Advanced degree in Computer Science, Software Engineering, Data Science, or a related field (Master’s preferred, PhD a plus).
- Strong proficiency in Python and experience building reusable packages, automation tools, and production ML pipelines.
- Deep understanding of ML system design, including model lifecycle management, MLOps practices, and scalable inference architectures.
- Hands-on experience with cloud platforms such as AWS, Azure, or GCP, along with containerization and CI/CD workflows.
- Familiarity with distributed computing frameworks, relational and non-relational databases, and large-scale data processing systems.
- Experience with generative AI frameworks such as LangChain, LangGraph, Hugging Face, or similar tooling for LLM-based applications.
- Strong analytical and problem-solving skills with the ability to decompose complex systems into practical engineering tasks.
- Excellent collaboration and communication skills, with experience working in cross-functional, product-oriented environments.
- Exposure to energy, utilities, or infrastructure domains is a strong plus.
Responsibilities
- Design, build, and deploy scalable AI and machine learning systems that support predictive analytics, optimization, and generative AI use cases in production environments.
- Develop reusable Python libraries, data pipelines, and modular components to support ML workflows and accelerate engineering productivity.
- Contribute to the design and implementation of AI system architectures, including RAG pipelines, LLM integrations, and agent-based workflows.
- Build evaluation, monitoring, and testing frameworks for ML and AI systems, focusing on performance, reliability, consistency, and fairness.
- Collaborate with cross-functional teams to translate complex business and technical requirements into actionable ML and software solutions.
- Optimize data storage, retrieval, and processing through effective database design and query performance improvements.
- Stay current with advances in machine learning, generative AI, and MLOps, integrating relevant innovations into production systems.
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