AI Engineer - AI Foundations and Platform Enablement

## 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

• - 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.