AI Engineer

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.