Agentic AI Engineer

Job Title

Pay Range: $52.00hr - $57.00hr

• Strong full-stack software engineering background (front-end, back-end, APIs, data layer) with hands-on coding ability.
• 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.
• 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 verbal and written communication skills.
• 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.

• Partner with business stakeholders across the organization 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 to 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 including Client, build, deployment, and post-launch monitoring/tuning.
• Act as a trusted technical voice for agentic AI within the organization, 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.

• Agentic AI.
• LLM.
• APIs.
• AI governance.
• Lang Chain.
• Lang Graph.
• Auto Gen.
• Semantic Kernel.
• AWS.
• Azure.
• GCP.
• RAG pipelines.