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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and deploying AI agents and agentic systems, with a strong focus on system architecture, integration of LLMs, and orchestration workflows. Proficient in evaluating AI system performance and reliability while collaborating effectively with multidisciplinary teams.
Highest-signal resume keywords
AI Agent DevelopmentSystem ArchitectureLLM IntegrationAgent OrchestrationPython Proficiency
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Software ArchitectureCloud DeploymentAsynchronous ProcessingAutomated TestingContext EngineeringRetrieval-Augmented GenerationAPI IntegrationInfrastructure DesignAgentic ArchitecturesTool Calling
Soft Skills
CollaborationCommunicationProblem-SolvingMentoringInitiative
Tools & Technologies
OpenAI APIsMicrosoft Agent FrameworkLangChainAWSAzureGoogle CloudCI/CDCodexClaude CodePi
Industry Keywords
Agentic EngineeringAI SolutionsObservabilityPerformance MonitoringQuality Assurance
Tech Stack
Tools & technologiesAWSAzureCloudPython
About the role
Key responsibilities & impact- Design, develop, and deploy production-grade AI agents and agentic platforms and workflows for complex business challenges
- Architect systems combining agentic harnesses, LLMs, tool calling, external APIs, enterprise data, and existing software applications
- Build agent orchestration workflows including state management, context engineering, memory, and human-in-the-loop interactions
- Define and implement evaluation, testing, observability, and monitoring practices for agent reliability, performance, cost, and security
- Make architectural decisions balancing autonomy, predictability, scalability, cost, and maintainability
- Collaborate with clients and multidisciplinary teams to identify opportunities, assess technical feasibility, and translate business needs into practical AI solutions
- Own technical delivery from solution design and prototyping through deployment and continuous improvement
- Evaluate emerging models, frameworks, and development approaches
- Share knowledge, mentor colleagues, and contribute to Osedea's Agentic AI development practices
Requirements
What you’ll need- 5+ years of professional software architecture and development experience
- Hands-on experience building production-grade agentic systems
- Strong software development and architecture fundamentals, including system architecture, APIs, asynchronous processing, testing, and cloud deployment
- Capacity for end-to-end infrastructure design: storage, compute, identity & policy management
- Demonstrated experience designing and delivering AI-powered applications beyond the proof-of-concept stage
- Solid understanding of agentic architectures, including tool use, orchestration, context management, and retrieval-augmented generation (RAG)
- Experience integrating harnesses, agents or LLMs with external services, internal systems, and existing enterprise data and infrastructure
- Ability to evaluate AI system quality and reliability, troubleshoot complex behaviours, and make informed technical trade-offs
- Comfortable working with fast-evolving technologies, tools, standards and best practices
- Builder mindset with strong architectural credentials and proficiency with agentic engineering building blocks and how they fit together
- Strong collaboration and communication skills, including the ability to navigate ambiguity, explain technical decisions to clients and teammates, and take initiative
- Intermediate or conversational French (minimum)
- Proficiency in Python and modern backend development practices
- Proficiency with LLM APIs and SDKs such as OpenAI, Anthropic Claude, or Google Gemini
- Proficiency with agent orchestration frameworks such as Microsoft Agent Framework, LangGraph/LangChain, OpenAI Agents SDK, or equivalent
- Proficiency with harnesses and plugin design using platforms such as Codex, Claude Code, or Pi
- Knowledge of ACP, MCP, Skill standards and other agentic engineering best practices
- Context engineering methods, including cache hit optimization
- Retrieval methods: RAG, embeddings, vector search, graph search and lookup
- Tool integration: function calling, API integrations, and patterns for connecting agents to external tools
- Quality and reliability practices: automated testing, agent evaluations, tracing, observability, and error handling
- Infrastructure experience with AWS, Azure, or Google Cloud, CI/CD, and production deployment practices
- Academic background is not specifically required; proven technical skills, curiosity, and practical experience are valued over a specific academic background or fixed technology stack
Benefits
Comp & perks- Competitive Salary and contribution to your pension plan (RRSP)
- Flexible hours of work and choose how you work
- Work from anywhere up to 8 weeks
- Paid sabbatical
- Wellness and productivity spending account
- Parental program
- Activities
- Training