AI Engineer - AI Foundations and Platform Enablement

AI Engineer - AI Foundations and Platform Enablement –

Location

Dallas, TX and Austin, TX

Hire type

C2H and FTE

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-ready patterns across MCP, orchestration, security, caching, and telemetry.

Key Responsibilities

Name of the candidate Contact# Email ID Location Availability Visa Rate Supplier Name

• Build POCs for MCP gateways and retail-domain MCP servers that securely expose enterprise 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 data freshness 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, tool calls, latency, errors, and policy decisions.
• Deliver reusable APIs, reference implementations, documentation, and standards for application teams.
• Partner with architecture, security, product, and engineering teams to move POCs toward 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 distributed systems.
• Practical knowledge of authentication, authorization, secrets management, and secure service communication.
• Experience with observability, including structured logging, metrics, tracing, and operational dashboards.
• Experience with cloud and containerized deployments, such as Kubernetes.
• Strong communication and collaboration skills, with the ability to turn ambiguous ideas into working POCs.

Nice to Have

• Experience with Model Context Protocol, MCP gateways, MCP servers, or similar agent 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 regulated industry.
• Familiarity with responsible AI, data privacy, model governance, vector databases, embeddings, and retrieval systems.
• Experience with CI/CD, infrastructure as code, automated testing, and performance testing.

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.