Forward Deployed Engineer

Salary: $90,800 - 130,000 per year

Requirements:

• 5–10+ years of experience in software engineering, cloud architecture, or AI engineering, including hands-on development of generative AI solutions; 5+ years is specified in the preferred qualifications.
• Strong proficiency in Python, REST APIs, FastAPI, asynchronous programming, microservices, distributed systems, and event-driven architectures.
• Strong understanding of SQL, NoSQL, Git, Docker, and automated testing.
• Hands-on experience building LLM-powered applications and AI agents, including RAG, vector databases, embeddings, tool calling, and prompt engineering.
• Knowledge of LangGraph, Semantic Kernel, Microsoft Agent Framework, AutoGen, or comparable orchestration frameworks; understanding of multi-agent collaboration, planning, state management, and agent lifecycle management.
• Experience evaluating and debugging non-deterministic AI systems and delivering AI solutions into enterprise production environments.
• Experience with Azure OpenAI, Azure AI Foundry or Microsoft Foundry, Azure AI Search, Azure Functions, Container Apps or AKS, API Management, Azure Storage, Cosmos DB or Azure SQL, Microsoft Entra ID, Key Vault, Azure DevOps or GitHub Actions, and Azure Monitor or Application Insights.
• Knowledge of API integration, enterprise authentication, OAuth 2.0, OpenID Connect, managed identities, RBAC, secure handling of sensitive data, networking, private endpoints, and Azure landing-zone principles.
• Ability to independently build, deploy, and troubleshoot AI solutions; engage confidently with business and technical stakeholders; and deliver secure, observable, reliable, maintainable production systems in ambiguous environments.
• Bachelors or Masters degree in Computer Science, Engineering, AI, or a related discipline is preferred.
• Azure certifications, such as Azure AI Engineer Associate or Azure Solutions Architect Expert, are preferred.
• Experience with Terraform, Bicep, or other Infrastructure-as-Code tools; enterprise AI governance, responsible AI, or regulatory compliance; and complex enterprise sectors such as financial services, insurance, healthcare, or manufacturing is preferred.
• Experience developing agentic solutions for document intelligence, enterprise knowledge retrieval, or workflow automation is preferred.

Responsibilities:

• Design and build autonomous or semi-autonomous AI agents, multi-agent architectures, and workflows with human approvals, memory, state management, and dynamic task execution.
• Implement tool calling, function execution, Model Context Protocol, agent-to-agent communication, enterprise RAG, agent evaluation, observability, guardrails, and continuous improvement.
• Develop and deploy AI applications and cloud-native services on Microsoft Azure, including secure integrations, data services, identity and governance controls, CI/CD pipelines, and monitoring.
• Work directly with enterprise clients to identify AI automation opportunities, lead discovery sessions and requirements workshops, and translate business needs into technical designs, delivery plans, and measurable outcomes.
• Rapidly prototype AI capabilities, demonstrate business value, and work alongside client engineering teams to support implementation and adoption.
• Integrate AI solutions with enterprise applications, APIs, databases, platforms, and business workflows, including systems such as SAP, Salesforce, ServiceNow, and Microsoft Dynamics.
• Troubleshoot production issues across AI models, application services, and cloud infrastructure; own delivery from proof of concept through production deployment and operational handover.
• Build intelligent document-processing, decision-support, and workflow-automation solutions with resilient execution, audit trails, exception handling, and escalation workflows.
• Develop production-quality applications, reusable agent components, connectors, orchestration services, and deployment templates, primarily using Python and FastAPI.
• Create automated tests and evaluation frameworks for agent workflows, tools, prompts, and integrations; assess accuracy, task completion, hallucination risk, latency, cost, and reliability.
• Apply LLMOps and DevSecOps practices, optimize model and inference costs, and maintain technical documentation, architecture diagrams, and deployment runbooks.

Technologies:

• Agentic AI
• AI
• AI Agents
• API
• Architect
• Azure
• CI/CD
• Cloud
• Cosmos DB
• DevSecOps
• DevOps
• Docker
• FastAPI
• Git
• GitHub
• Support
• LLM
• NoSQL
• OAuth
• OpenID
• Python
• RAG
• REST
• RBAC
• SAP
• SQL
• Salesforce
• ServiceNow
• Terraform
• microservices
• Backend
• Kubernetes
• MCP
• OpenTelemetry
• Security

More:

We are hiring a full-time Forward Deployed AI Engineer to deliver enterprise-grade Agentic AI solutions in client environments. This hands-on, customer-facing role spans discovery, prototyping, integration, deployment, monitoring, and ongoing improvement, bringing together AI engineering, enterprise architecture, software development, and client delivery. We are looking for builders who can turn agentic AI concepts into measurable enterprise outcomes. The position is hybrid, remote, or client-site as required, with the listed work location in Oshawa, Ontario. Pay is $90,800–$130,000 per year. Benefits include casual dress, dental care, extended health care, paid time off, and vision care.

last updated 45 week of 2026