Forward Deployed Engineer

Spectraforce

Charlotte, NC, United States

2 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

leoforce.us

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Application Layers Microsoft Azure Software Deployment Systems Integration Large Language Models Multi-Agent Systems Backend Data Layers Kubernetes

+3 more

Front End Software Development Software Coding Data Pipelines

Job description

Partner with business stakeholders across Prosperity to understand workflows and pain points, and proactively identify areas where agentic AI can drive meaningful improvement.
Design, build, and deploy AI agents that automate or augment business processes - from initial concept through production deployment.
Work full-stack: build the agent logic/orchestration as well as the surrounding application layer, integrations, APIs, and data pipelines needed to make an agent usable in a real workflow.
Rapidly prototype agentic solutions, validate them with business users, and iterate quickly based on feedback.
Evaluate and select appropriate agent frameworks, tooling, and LLM/model choices for each use case.
Partner with platform/architecture teams to ensure agents are secure, scalable, auditable, and aligned with enterprise AI governance standards.
Own technical delivery of assigned use cases end-to-end - discovery, build, deployment, and post-launch monitoring/tuning.
Act as a trusted technical voice for agentic AI within Prosperity, educating business partners on realistic capabilities and limitations.
Identify patterns across use cases and feed reusable agent components/frameworks back to the broader engineering organization.

Requirements

Strong full-stack software engineering background (front-end, back-end, APIs, data layer) with hands-on coding ability - this is a builder role, not an advisory one.
Direct, hands-on experience building AI agents or agentic workflows (e.g., using LLM orchestration frameworks, tool-calling/function-calling, RAG pipelines, or multi-agent systems).
Demonstrated ability to independently identify business opportunities for AI/agentic involvement - not just execute a pre-defined technical spec.
Comfort working directly with business stakeholders to scope ambiguous problems and translate them into working solutions.
Experience with cloud platforms (AWS/Azure/GCP) and modern data/AI tooling.
Strong communication skills - able to explain agentic AI concepts, tradeoffs, and limitations to non-technical business partners.
Comfort with fast iteration cycles and evolving requirements typical of emerging-technology initiatives.
Prior experience in a Forward Deployed Engineer, Applied AI Engineer, or Solutions Engineer role.
Experience with agent frameworks/tooling such as LangChain, LangGraph, AutoGen, Semantic Kernel, or similar.
Domain knowledge in Wealth Management, Retirement Services, or broader financial services.
Experience with enterprise AI governance, model risk, or responsible AI practices in a regulated industry.
Prior startup, consulting, or client-facing delivery experience.