Lead Agentic AI Developer

Architect and Build Agentic Systems: Design and implement agentic AI architectures capable of reasoning, planning, and self-adaptation within firm-approved security and compliance boundaries.

• Leverage and Extend Frameworks: Utilize and enhance frameworks such as Strands, CrewAI, LangGraph, and Agent Core to create standardized, reusable components that accelerate agentic development across teams.
• Cloud Engineering: Develop secure, resilient, and scalable cloud-native solutions using AWS services (Lambda, ECS, S3, API Gateway, SageMaker, Bedrock, etc.) to support production-grade AI operations.
• Monitoring and Evaluation: Implement metrics, tracing, and evaluation pipelines that ensure transparency, reliability, and continuous improvement in agentic behavior.
• Integration and Governance: Collaborate with security, risk, and compliance to embed governance, auditability, and ethical safeguards into all systems.
• Collaboration: Partner with data science, enterprise architecture, and application engineering teams to integrate agentic capabilities into firm platforms.
• Innovation Leadership: Research, test, and recommend new frameworks and patterns that responsibly advance the firm's AI capabilities.
• Ownership: Drive full lifecycle delivery-from technical design through deployment and iteration-maintaining high standards of reliability and documentation.

Other responsibilities as assigned.

Impact and Opportunity This role is at the leading edge of Raymond James' AI evolution-building systems that convert the firm's collective intelligence into secure, adaptive knowledge that serves clients better every day.

It's demanding work-technically complex, mission-critical, and transformative-but it's work that matters. You'll have the opportunity to shape the systems that define how knowledge flows, how teams collaborate, and how the firm competes at the top of the industry for years to come.

Do you have experience in Python?, Proficiency in Java or Python (with experience in TypeScript or Go a plus).

• Solid understanding of AWS architecture and services-deployment, monitoring, security, and cost optimization.

• Familiarity with agentic or LLM frameworks such as Strands, CrewAI, LangGraph, Agent Core, or similar.

• Experience with retrieval systems, vector databases, embeddings, and orchestration frameworks.

• Strong grounding in secure API design, data modeling, and CI/CD automation.

• Proven record of writing clean, testable, production-grade code.

• Experience designing tool/function-calling integrations and Model Context Protocol (MCP) servers and connectors.

• Strong prompt and context engineering, including agent memory, state, and session/context management.

• Experience with agent and LLM evaluation, guardrails, and safety controls-hallucination mitigation, content filtering, and human-in-the-loop oversight.

• Hands-on experience with foundation models via Amazon Bedrock (e.g., Anthropic Claude), including model selection and prompt/parameter tuning for latency and cost.

• Familiarity with multi-agent orchestration and workflow design-planning, task decomposition, routing, tool use, and error handling.

• Experience with containerization and orchestration (Docker, Kubernetes) for deploying scalable, resilient services.

• Knowledge of Terraform for IaC deployments. Hands-on experience with Terraform is a plus.

• OpenTelemetry for Dynatrace and Observability

• Experience with agent performance tuning and cost optimization.

• Experience with time-boxed A2A and Swarm/Crew Agentic solutions

• Deploying large language models on-prem

• Experience with fine-tuning LLMs - nice to have

• Mindset: Deep curiosity, bias toward execution, and respect for precision and reliability.

• Financial services experience preferred but not required.

Competencies and Behaviors:

• Analytical Thinking: Deconstruct complex technical and business challenges into clear, scalable solutions.
• Communication: Translate advanced technical concepts into shared understanding across business, technology, and risk partners.
• Judgment: Balance innovation with security, compliance, and long-term maintainability.
• Technical Mastery: Maintain and expand expertise in agentic AI, cloud engineering, and software craftsmanship.
• Collaboration: Work cross-functionally with integrity and respect, ensuring outcomes align with the firm's mission and standards.
• Client Focus and Integrity: Make decisions grounded in what best serves clients and the firm's long-term stability-acting with transparency and accountability.

Education/Background: Master's degree in Computer Science, Engineering preferred, or Bachelor's degree in Computer Science, Engineering is required.

Experience: 8+ years of professional software engineering, with demonstrated success in building scalable distributed systems with at least 1 year in AI-powered platforms.