AI Engineer / Anthropic Architect
New
CanadaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of experience in software engineering, solutions architecture, technical consulting, or customer-facing AI roles
- Required Skills
- AWSPythonGCPAzurePrompt EngineeringLangChain
Requirements
- 5+ years of experience in software engineering, solutions architecture, technical consulting, or customer-facing AI roles.
- 2+ years of hands-on experience building production LLM applications using Claude, GPT-4, or similar enterprise-grade models.
- Strong Python programming skills, with additional experience in TypeScript, Java, or similar languages being a plus.
- Experience designing and deploying agentic systems using frameworks such as LangGraph, LangChain, CrewAI, or DSPy.
- Solid understanding of cloud platforms (AWS, Azure, or GCP) and enterprise integration patterns.
- Experience with prompt engineering, evaluation frameworks, LLM observability, and scalable AI deployment.
- Proven ability to lead technical discovery sessions and translate business requirements into architecture decisions.
- Strong communication skills, with the ability to explain complex AI concepts to both technical and executive audiences.
- High autonomy, strong ownership mindset, and comfort working in ambiguous, fast-moving customer environments.
Responsibilities
- Embed directly with enterprise customers to design, build, and deploy production AI applications powered by Claude and other LLM systems.
- Lead the architecture and implementation of scalable AI solutions, including agentic workflows, RAG systems, MCP servers, and multi-agent frameworks.
- Deliver rapid prototypes, demos, and proof-of-concept solutions that demonstrate clear business value and technical feasibility.
- Act as the primary technical point of contact across customer engagements, coordinating with internal delivery, sales, and partner teams.
- Design enterprise-grade AI architectures and integration patterns across cloud platforms (AWS, Azure, GCP) and enterprise data ecosystems.
- Conduct technical discovery workshops to define customer requirements, assess AI readiness, and translate needs into architecture roadmaps.
- Respond to RFPs, RFIs, and security reviews with clear and compelling technical documentation.
- Provide guidance on LLM evaluation, prompt engineering, observability, and production deployment best practices.
- Identify technical risks early and ensure scalable, reliable delivery of AI solutions in production environments.
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