AI Native Developer (Agentic / Applied)
Role Overview
Build production agentic architectures for real enterprise client environments, rather than demos or pilots that do not reach production.
Design, build, and deploy enterprise agentic AI systems across the technology stack, working directly with client development teams.
Build reusable patterns and accelerators that scale beyond individual engagements.
Key Skills for This Role
Full Job Posting
Role overview
Build production agentic architectures for real enterprise client environments, rather than demos or pilots that do not reach production.
Design, build, and deploy enterprise agentic AI systems across the technology stack, working directly with client development teams.
Build reusable patterns and accelerators that scale beyond individual engagements.
Responsibilities
- Build end-to-end agentic systems with multi-agent orchestration, RAG, policy-based routing, tool invocation, memory, and lifecycle observability.
- Own RAG pipelines, including embeddings, chunking, vector search, and context-window design tuned to quality targets.
- Integrate and abstract across LLM providers, with fallback routing and token, cost, and latency management.
- Implement production LLM Ops, including evaluation metrics, prompt versioning, observability, and cost and safety monitoring.
- Work with client teams on workshops, proofs of concept, code-with sessions, and architecture walkthroughs to design and deploy agentic solutions.
- Create reusable patterns, accelerators, and playbooks for future engagements.
- Measure agent accuracy, latency, safety, and cost-effectiveness and present findings to client stakeholders.
Required qualifications
- 7+ years of production software development experience and at least 1 year designing and deploying production agentic AI solutions.
- Experience with orchestration frameworks such as LangGraph, CrewAI, AutoGen, or equivalent.
- Experience integrating production LLM APIs, operating RAG pipelines, and implementing LLM Ops practices.
- Experience with Kubernetes, Docker, microservices, serverless architectures, CI/CD, and infrastructure as code such as Terraform or Helm.
- Proficiency in Python; Java or an equivalent backend language is acceptable.
- Experience debugging and monitoring production systems and deploying multiple production agentic AI systems.
- Experience standardizing RAG, LLM integration, or LLM Ops practices; working directly with client development teams; and building reusable frameworks or playbooks.
- Experience defining and tracking AI performance metrics such as accuracy, latency, safety, and cost.
- Bachelor's degree or college diploma in a related field.
- English is required for work with multinational North American teams.
Compensation
- The posting gives an annual base pay range of CAD 100,300–216,700 for British Columbia, Ontario, and New Brunswick.
- Compensation varies based on office location, role, skills, and experience; the range is a guideline and is based on full-time employment.
Opportunity
The role offers work across industries and enterprise technology stacks, vendor fellowship access with Anthropic, OpenAI, Microsoft, and Google technical teams, and a pathway to the Forward Deployed Developer program.
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