4 days ago
Calgary, Canada or Vancouver, CanadaStaff+
Responsibilities
- Design and oversee end-to-end ML architecture covering data ingestion, feature stores, model serving, monitoring, and lifecycle management.
- Define the long-term roadmap for AI infrastructure and orchestration frameworks.
- Build extensible, self-service AI primitives including APIs, SDKs, and standardized patterns for engineering teams.
- Establish scalable, testable, and maintainable MLOps practices, including model versioning, A/B testing, drift detection, and automated rollbacks.
- Architect and evolve LLM-powered copilots, search systems, assistants, and agents using RAG pipelines, tool integrations, and multi-step reasoning.
- Design evaluation frameworks for GenAI systems using offline benchmarks, online metrics, and human feedback.
- Develop prompt engineering, agent design, orchestration, safety, prompt-injection defense, and hallucination-mitigation practices.
- Lead technical design reviews, mentor engineers, and drive performance, reliability, security, compliance, and responsible AI improvements.
- Partner with Product Managers, Data Scientists, and ML Engineers to translate business problems into scalable technical solutions.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field, or equivalent deep professional experience.
- 8+ years of software engineering experience, including at least 4+ years architecting and deploying production ML models at scale.
- 2+ years in a technical leadership role involving scalable platforms, technology transformation, or modernization initiatives.
- Staff- or Senior-level experience with technical leadership, architecture ownership, mentoring, and internal platform development.
- Deep expertise in MLOps and production ML systems, including training, evaluation, deployment, monitoring, and lifecycle management.
- Strong experience with AWS or Google Cloud and scalable distributed AI/ML workloads.
- Experience with data architecture and engineering, including pipelines, feature engineering, data modeling, and large-scale processing.
- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
- Experience with ML infrastructure, feature stores, experiment tracking, model registries, orchestration frameworks, and ML deployment pipelines.
- Experience designing LLM systems, GenAI evaluations, observability, prompt engineering, RAG systems, embedding strategies, vector databases, and retrieval optimization.
- Experience building agentic workflows with multi-step reasoning, tool use, and orchestration frameworks such as LangChain, LlamaIndex, ADK, or custom frameworks.
- Strong understanding of distributed systems, APIs, microservices architecture, reliability, scalability, data governance, model governance, security, privacy, bias, and explainability.
- Excellent communication, collaboration, stakeholder influence, and cross-functional alignment skills.
- Relevant cloud platform or technology certification is a preferred qualification.
Benefits
- Flexible hybrid work approach with no fixed in-office requirement for employees located near an office.
- Innovative work, growth opportunities, caring coworkers, and purpose-driven technology.
- Employee resource groups and a stated commitment to diversity, equity, inclusion, and belonging.
- Accessible hiring and assessment accommodations are available for candidates with disabilities.
Categories
About Benevity
Benevity builds an enterprise SaaS platform for corporate social impact, covering employee giving, volunteering, grants management, ERGs, and donation processing with global charity vetting. Companies use it to run and measure programs for employees and customers, with integrations and payment rails. Founded in 2008 and headquartered in Calgary, it is privately held and part of Hg’s portfolio, serving many large, multinational brands.