GenAI-Powered Python Architect; Azure, LangChain

Job Summary:

• 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.

Required

Skills & Experience:

• 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 Lang Chain, Lang Graph, Auto Gen, 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.