Forward Deployed Engineer (FDE) – AI

CGI•Buffalo, NY

14h•Hybrid

About The Position

CGI is looking for a Forward Deployed Engineer (FDE) – AI to support our client. You will work directly with business and technology teams to understand business problems, translate them into AI solutions, and deliver those solutions into production. As an FDE, you will own AI use cases end-to-end—from discovery and solution design through development, governance, production deployment and adoption. You will build hands-on solutions using LLMs, RAG, Agentic AI, APIs, data pipelines and model integrations, while working across business, architecture, security and governance teams. This is a Full-Time, on-site employment opportunity based in Buffalo, NY (Preferred) or any CGI office in East Coast in a Hybrid Model with travel to Buffalo as required.

Requirements

At least 5+ years in software engineering, Applied AI, or solutions engineering experience.
Hands on experience building applications that use LLMs or foundation models (prompt engineering, RAG pipelines, agent frameworks, tool calling).
Experience working directly with business stakeholders to translate a problem into a technical solution.
Ability to work across the full stack: API integration, data pipeline, model integration, front end or workflow integration.
Comfort with governance and compliance processes. You navigate risk reviews and architecture approvals as part of delivery, not as obstacles.

Nice To Haves

Experience in financial services, banking, or another regulated industry.
Experience with agentic AI patterns: multi step workflows, tool use, autonomous execution with guardrails.
Familiarity with the AI platform stack (Azure AI Foundry, Anthropic Claude, Azure APIM) or comparable enterprise AI platforms.
Experience mentoring or training others on AI delivery practices. As the hub and spoke model scales, FDEs train divisional engineers.

Responsibilities

Embed directly with client or business unit teams to scope, build, and iterate on AI solutions in close proximity to the end users and their actual workflows.
Translate ambiguous business problems into working prototypes quickly, prioritizing speed to value over polished long term architecture in early stages.
Own the full delivery loop for assigned use cases: requirements gathering, prompt/pipeline design, integration with client data and systems, testing, and deployment support.
Work hands on with the AI platform (models, RAG pipelines, agents, orchestration tools) to build and harden solutions, escalating platform gaps back to central engineering.
Act as the technical face of the AI team to the client by running working sessions, demoing iterations, and adjusting scope based on real time feedback.
Identify where a client's request requires governance review, security sign off, or platform changes, and route it through the appropriate process.
Document solution designs, data flows, and decisions well enough for handoff to a sustaining engineering or platform team once a solution stabilizes.
Provide field feedback that shapes platform, governance, and tooling priorities, acting as the connective tissue between client reality and internal roadmaps.