AI Solution Principal

Job Overview

We are seeking an AI Solution Principal I for the Anthropic Ecosystem team in Canada. This is a hands-on deep-tech role supporting the Distinguished Engineer, Agentic AI, and the MSAB pillar.

The role will design and build advanced agentic workflows, MCP / tool integrations, orchestration patterns, and reusable technical components for Digital Employees. The candidate will create reference implementations that FDE teams can deploy and GTM teams can position with clients.

This role is highly technical and delivery-enabling. It does not carry P&L or commercial ownership.

Key Responsibilities

• Design and build advanced agentic workflows using Claude, MCP tools, and enterprise integration patterns.
• Build reusable MCP / tool integrations across enterprise systems, APIs, data platforms, workflow tools, and knowledge repositories.
• Create reference implementations that can be reused by FDE teams and positioned by GTM teams.
• Develop reusable Digital Employee components such as task planners, tool routers, approval flows, escalation logic, memory patterns, and domain workflows.
• Support the Distinguished Engineer in building and validating MSAB platform assets, architecture patterns, and reusable components.
• Define and implement agentic orchestration patterns for multi-agent workflows, human-in-the-loop processes, and tool-use governance.
• Support eval patterns, eval harnesses, observability, system design, performance tuning, and reliability engineering.
• Feed deep technical learnings from client work back into MSAB, CoE, field teams, and delivery teams.
• Support joint architecture work with Anthropic engineering team.
• Build technical blueprints, demo assets, prototypes, reusable code components, and implementation guides.
• Ensure reusable components meet standards for security, scalability, reliability, observability, and maintainability.

Scope of Ownership

Owns

• Advanced agentic workflow design and build
• MCP / tool integration implementation
• Reference implementations for FDE deployment
• Reusable Digital Employee components
• Agentic orchestration patterns
• Eval pattern implementation support
• Observability and performance tuning patterns
• System design support for MSAB assets
• Technical feedback loop into MSAB, CoE, and field teams
• Joint architecture contribution with Anthropic engineering

Does Not Own

• P&L or commercial accountability
• GTM strategy - owned by Principal GTM
• FDE utilisation - owned by Director FDE Global
• Overall Agentic AI technical strategy - owned by Distinguished Engineer, Agentic AI
• Final commercial pricing and contracting
• Day-to-day FDE scheduling
• Client account ownership

Required Skills and Experience

• 10+ years of experience in solution architecture, AI engineering, platform engineering, enterprise integration, software engineering, or advanced technical consulting.
• Strong hands-on experience designing and building GenAI, LLM, agentic AI, automation, or intelligent workflow solutions.
• Experience with Claude, LLM applications, prompt design, tool use, agent orchestration, and enterprise AI implementation patterns.
• Strong understanding of MCP-style integrations, secure tool access, permissions, APIs, and enterprise connectivity.
• Experience building reusable reference implementations, accelerators, prototypes, SDK components, or technical solution blueprints.
• Knowledge of evals, observability, logging, tracing, system reliability, and performance tuning for AI systems.
• Ability to design reusable Digital Employee components and agentic workflow patterns.
• Strong software engineering skills with experience in Python, TypeScript, APIs, cloud-native services, or integration frameworks.
• Ability to work across Distinguished Engineering, FDE, GTM, CoE, delivery, and partner engineering teams.
• Strong communication skills with the ability to explain complex technical topics clearly.

Preferred Skills and Experience

• Experience with Anthropic, Claude, MCP, Agentic AI, Digital Employees, or enterprise AI assistants.
• Experience building multi-agent systems, tool-using agents, copilots, autonomous workflows, or enterprise AI agents.
• Experience with cloud platforms, vector databases, RAG, workflow orchestration, event-driven systems, and enterprise data platforms.
• Exposure to AI evaluation frameworks, model benchmarking, red-teaming, responsible AI, and quality gates.
• Experience in consulting, systems integration, SaaS, enterprise platform, or AI transformation environments.
• Experience supporting client demos, POCs, pilots, and production deployments.
• Experience working with strategic technology partners or partner engineering teams.