Forward Deployed Engineer — AI Solutions
M&T Bank · Buffalo, United States · 24d ago
Mid levelHybridSoftware Engineering
This role sits at the intersection of business strategy, AI innovation, and execution. Embedded within a business line, you'll identify opportunities, design and develop AI solutions, navigate enterprise governance processes, and lead implementations through production deployment. You will own outcomes end-to-end, serving as the critical link between business demand and governed, scalable AI solutions that create measurable impact.
Skills and Indicators
- Business fluency: You can sit in a room with a Commercial Banking RM or a Risk Officer and understand their problem in their language. You translate it into an AI solution design, not the other way around.
- Delivery ownership: You do not hand off. You own the use case from problem statement through production adoption. If it stalls in governance, you unstick it. If adoption lags, you fix it.
- Builder mentality: You prototype fast, iterate with stakeholders, and ship governed solutions. You are not a consultant who writes a deck and leaves.
- Pattern thinking: Every use case you deliver produces a reusable pattern for the next one. You document what works and feed it back to the hub.
Required Qualifications
- Associate’s degree and a minimum of 7 years’ systems analysis and/or application development work experience or Bachelor's degree and a minimum of 5 years' systems analysis and/or application development work experience. In lieu of a degree, a combined minimum of 9 year’s education and/or relevant work experience, including a minimum of 5 years’ system analysis and/or application development work experience.
- 5+ years in software engineering, applied AI, or solutions engineering. You have shipped production software, not just prototypes.
- Hands-on experience building applications that use LLMs or foundation models (prompt engineering, RAG pipelines, agent frameworks, tool calling). You have built at least one system that calls a model in production.
- Experience working directly with business stakeholders to translate a problem into a technical solution. You can run a discovery session, not just take a requirements document.
- Ability to work across the full stack: API integration, data pipeline, model integration, front-end or workflow integration. You are not specialized in one layer.
- Comfort with governance and compliance processes. You navigate risk reviews and architecture approvals as part of delivery, not as obstacles.
Preferred Qualifications
- Experience in financial services, banking, or another regulated industry. You understand why governance exists and can work within it productively.
- Experience with agentic AI patterns: multi-step workflows, tool use, autonomous execution with guardrails.
- Familiarity with the M&T 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.