Principal Engineer I - App Engg & AI

Responsibilities

• Own architecture decision records and reference architecture for the platform; drive review with enterprise architecture teams at Fortune 500 clients.

• Build production code. This role is roughly 70% hands-on   you set direction by shipping the hard parts,

• Define the evaluation strategy: offline eval harnesses, regression suites for non-deterministic systems, and the quality bar that gates promotion.

• Design multi-tenant isolation, identity, and authorization patterns (OIDC/PKCE, M2M, JIT provisioning) suitable for data-sensitive clients.

• App Development: Build functional applications using large language models (LLMs) and APIs (e.g., OpenAI, Anthropic, open-source Llama).

• Model and control unit economics   per-session token cost, agentic loop multipliers, model tiering   so engagements stay margin-positive.

• Mentor senior and lead engineers; raise the ceiling of the org, not just the output of a project.

• Partner with go-to-market on technical positioning, competitive differentiation, and executive-level architecture narratives.

 
 
 
Required Qualifications

• 12  years building and operating production distributed systems; 3  years shipping LLM-based systems.

• Deep experience with at least one major cloud AI stack (AWS Bedrock / AgentCore, Azure AI Foundry, or equivalent) and fluency in the tradeoffs of the others.

• Strong grounding in one server-side language at production depth (Java/Spring Boot, Python, TypeScript) plus infrastructure-as-code.

• Practical experience with agentic patterns: tool use, multi-step planning, orchestration, human-in-the-loop approval flows, MCP or comparable protocol work.

• Track record designing for compliance-sensitive environments   audit logging, data residency, least-privilege authorization, policy enforcement.

• Ability to hold a technical argument with a client's chief architect and a business argument with their CFO in the same meeting.

Preferred

• Prior work on compilers, type systems, policy engines, or CI/CD platforms   the mental model transfers directly.

• Consulting or professional services background; comfort with multi-client context switching.

• Experience with Snowflake, databricks or  data governance tooling, or enterprise data quality platforms.