Artificial Intelligence Engineer

AI Engineer - AI Foundations and Platform Enablement –Location: Dallas, TX and Austin, TX ## Role Summary Design and build the foundational platform layers needed to deliver secure, scalable,reusable AI use cases. The role will develop proofs of concept and production-readypatterns across MCP, orchestration, security, caching, and telemetry. ## Key Responsibilities - Build POCs for MCP gateways and retail-domain MCP servers that securely exposeenterprise tools and data.- Design an orchestration layer for coordinating models, agents, tools, workflows,approvals, retries, and failures.- Establish caching patterns that improve latency and cost while protecting datafreshness and privacy.- Implement agentic authentication and authorization, including identity propagation,delegated access, least privilege, and auditability.- Create telemetry for AI workflows, including traces, metrics, logs, token usage, toolcalls, latency, errors, and policy decisions.- Deliver reusable APIs, reference implementations, documentation, and standards forapplication teams.- Partner with architecture, security, product, and engineering teams to move POCstoward production. ## Must Have - 5+ years of software engineering experience building distributed services or platforms.- Hands-on experience with LLM applications, AI agents, RAG, or tool-calling workflows.- Strong programming skills in Java, Python, TypeScript, or Go.- Experience with APIs, service integration, asynchronous processing, and distributedsystems.- Practical knowledge of authentication, authorization, secrets management, and secureservice communication.- Experience with observability, including structured logging, metrics, tracing, andoperational dashboards.- Experience with cloud and containerized deployments, such as Kubernetes.- Strong communication and collaboration skills, with the ability to turn ambiguous ideasinto working POCs. ## Nice to Have - Experience with Model Context Protocol, MCP gateways, MCP servers, or similaragent integration frameworks.- Experience building orchestration or workflow platforms with durable execution,queues, event streams, or human-in-the-loop controls.- Experience with Redis or other distributed caching technologies.- Experience with Open Telemetry and AI observability or evaluation platforms.- Knowledge of OAuth 2.0, OpenID Connect, workload identity, token exchange,delegated authorization, or policy engines such as OPA.- Experience in financial services, retail investing, brokerage, or another regulatedindustry.- Familiarity with responsible AI, data privacy, model governance, vector databases,embeddings, and retrieval systems.- Experience with CI/CD, infrastructure as code, automated testing, and performancetesting. ## Expected Outcomes - Working POCs for an MCP gateway, retail MCP server, and orchestration layer.- Reusable patterns for secure agent access, caching, and AI telemetry.- A practical roadmap for hardening foundational capabilities for production adoption.