Lead AI/ML and MLOps Consultant
New
CanadaFull-TimeLead
Salary not disclosed
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Job Details
- Required Skills
- Data scienceMLOps
Requirements
- Extensive experience in AI/ML engineering, data science, or MLOps roles, with proven delivery of production-grade machine learning systems.
- Strong hands-on expertise across the ML lifecycle, including data pipelines, model development, deployment, monitoring, and optimization.
- Deep understanding of MLOps practices such as CI/CD for ML, model governance, feature stores, experiment tracking, and observability.
- Experience designing scalable cloud-based architectures for AI workloads using modern data and ML tooling.
- Proven ability to lead technical delivery teams and act as a senior technical authority in client-facing environments.
- Strong communication and consulting skills, with the ability to translate technical complexity into business value.
- Experience contributing to pre-sales activities such as scoping, solution design, and proposal writing.
- Comfort working in fast-paced consulting or delivery environments with multiple concurrent client engagements.
Responsibilities
- Lead the end-to-end design and implementation of machine learning systems, including data ingestion, feature engineering, model training, evaluation, deployment, and monitoring in production environments.
- Define and own technical architecture decisions across data platforms, ML infrastructure, serving layers, and MLOps toolchains.
- Establish engineering standards for ML delivery, including experiment tracking, model versioning, reproducibility, governance, CI/CD for ML, and drift monitoring.
- Provide technical leadership and mentorship to engineering teams, elevating best practices in modern ML engineering and MLOps maturity.
- Ensure solutions meet high standards for security, reliability, scalability, and operational excellence across client engagements.
- Lead pre-sales technical activities including discovery sessions, solution workshops, effort estimation, and proposal development.
- Translate business challenges into clear, defensible AI/ML architectures and guide client decision-making through technical expertise.
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