This is not a consulting role. It is not a project delivery role. It is not a research position. A Forward Deployed AI Engineer-Associate Manager is a production engineer who works embedded inside a client's enterprise, shoulder to shoulder with their teams, to make complex AI platforms work in real, messy organizational environments. You own outcomes: time-to-value, adoption, reliability, and scalability. Not delivery milestones. Outcomes.

The market is beginning to understand what leading technology companies have demonstrated: AI products fail not because the models are weak but because deployment is broken. The gap between a successful AI pilot and an AI capability that scales is bridged by engineers who can translate platform capability into measurable business value inside a real enterprise workplace. That is this role.

Forward Deployed AI Engineers form the execution spine of our Reinvention Deployment Engineering pods. We are building the largest FDE capability in the services industry. The engineers who join at this stage will define what the role looks like at scale and will have access to the hardest enterprise AI problems in the market across every industry.

Key Responsibilities

Lead enterprise AI platform deployments across complex multi-stakeholder client environments — Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir — owning the full programme from architecture through adoption and ongoing iteration, with accountability for quality, time-to-value, reliability, adoption velocity, and scalability across multiple concurrent workstreams, with commercial metrics attached.

Lead rapid experimentation at pace:



drive ambiguous business problems to working production systems in days or weeks across complex enterprise environments through hands‑on prototyping, iteration, and deployment of AI‑enabled workflows, agents, copilots, and automations into real operational environments.

Architect and govern enterprise AI solutions across the full technology stack: identity, data, security, governance, platform layer, and multi‑system workflow integration at programme scale, including prompt and workflow design, API integrations and orchestration, enterprise knowledge integration, and privacy considerations. Provide technical leadership and best practices for responsible, secure, and scalable AI deployment.

Shape AI reinvention strategy for client CTO, CFO, and CISO: build value architecture, ROI backlogs, use case prioritization frameworks, and multi‑year AI adoption roadmaps.

Codify delivery learnings, failure patterns, and engineering standards into reusable reinvention blueprints, patterns, and accelerators; define and publish these assets to scale across multiple client engagements, grow the FDE practice, and enable the next generation of forward deployed engineers.

Lead architecture design sessions, executive workshops, and code‑with sessions with client engineering and C‑suite leadership teams.

Work directly with clients and internal teams to understand business problems, shape AI use cases, and define technical solutions from ambiguous or evolving requirements.



Serve as the technical bridge between product, engineering, data, and business stakeholders, ensuring solutions are technically sound, usable, and aligned with business value.

Design and implement end‑to‑end AI solutions, integrating LLMs and AI services such as OpenAI, Anthropic, and Gemini with enterprise platforms including SAP, ServiceNow, Salesforce, data platforms, and internal systems.

Basic Qualifications

Bachelor’s degree or college diploma in a related field.

Minimum of 7 years of engineering experience with cloud‑native systems, including APIs, microservices, containerization, and serverless architectures, including a minimum of 5 years of software engineering experience using Java, TypeScript/JavaScript, or Python and 5 years of experience integrating APIs and building distributed, production systems.

Minimum of 1 year of experience designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.

Minimum 1 year of hands‑on experience applying AI‑native and agentic software development practices across the software development lifecycle, including spec‑driven development and the use of AI coding agents to accelerate software design, coding, testing, debugging, modernization, documentation, and delivery.

Minimum of 1 year of experience with AI platforms (OpenAI, Claude, Vertex AI, or open‑source models), including building abstraction layers for multi‑provider pipelines, with experience working with enterprise AI platforms such as Anthropic, OpenAI, Microsoft, Google, Salesforce, SAP, or Palantir.

Minimum of 7 years of experience leading, managing, and developing software engineering teams, in

📌 Forward Deployed AI Engineer (Calgary)
🏢 Accenture
📍 Calgary