Principal Software Engineer AI Platforms

Principal Software Engineer AI Platforms
Location: [City/Remote]
Department: Engineering
Reports To: Director of Engineering
The Opportunity
We are looking for a "10x" AI-Native Engineer to join our core Engineering team. You will serve as the lead architect and builder for a suite of Enterprise AI platforms focused on Assurance, Evaluation, and Synthetic Data.
In this role, you will not just "write code" you will architect systems and orchestrate AI coding assistants (specifically Cursor, Claude, or Cline) to deliver software at exceptional speed. You will treat code generation as a commodity, focusing your energy on high-level system design, complex integration, and production reliability.
This is a hands-on Engineering role. You will own the full lifecycle from architectural decisions to deployment, monitoring, and on-call reliability ensuring these platforms meet the rigorous standards of regulated enterprise environments.

Key Responsibilities
1. AI-Native Development Strategy
Leverage advanced AI coding tools (Cursor, Cline, etc.) to accelerate boilerplate, refactoring, and test generation.
Maintain a "higher layer" of control: You define the architecture (Pydantic models, API contracts, State management etc) and the implementation (prompt based).
Rapidly pivot logic across applications as product requirements evolve, ensuring codebases remain clean, modular, and consistent.

2. Full-Stack Architecture (Python & React)
Backend: Architect unified API services using Python (FastAPI), ensuring async performance and strict type safety.
Frontend: Deliver responsive, modern client portals using React/Next.js (TypeScript).
Data Layer: Design complex schemas across PostgreSQL, Vector Stores, and Graph Databases.

3. Engineering Reliability & Operations
Infrastructure: Own the containerization (Docker) and orchestration (Kubernetes/Task Queues) for your services. You are responsible for ensure workers scale efficiently to handle heavy AI workloads.
Observability: Implement tracing and monitoring (e.g., OpenTelemetry) to debug complex chains of LLM calls and async workers.
Security: Enforce "Secure by Design" principles, particularly for PII/Data Privacy handling in regulated sectors. Ensure zero-trust architectures where necessary (e.g., zero-egress deployments).

4. Integration & Logic
Build abstract routing layers for LLMs (switching between OpenAI, Anthropic, and local models seamlessly).
Design agentic workflows that can "reason" about data without hardcoding rules.

Ideal Candidate Profile
The "Must-Haves":
AI-Native Workflow: You dont just use Copilot for autocomplete. You use coding agents to build entire features. You are comfortable reading/writing code generated by AI and spotting subtle bugs instantly.
Senior Engineering Foundation: You have 4+ years of deep experience in software delivery. You understand why "It works on my machine" is not acceptable. You know how to manage migrations, secrets, and CI/CD pipelines.

Stack Proficiency:
Backend: Python 3.11+, FastAPI, Pydantic (v2), SQLAlchemy (Async).
Frontend: React, Next.js, TypeScript, Tailwind.
AI/ML: Experience with RAG frameworks (Haystack/LangChain), Vector DBs, and LLM APIs (LiteLLM/OpenAI).

The "Differentiators":
System Thinker: You can mentally model how a change in one layer of the application platform affects another layer and refactor both accordingly.
Sovereign Builder: You can take a one-line requirement ("Add a maker-checker flow for compliance"), design the schema, generate the API, build the UI, and write the migration script independently.
Regulated Domain Fluency: Understanding of data privacy (PII masking), RBAC (Role Based Access Control), and audit trails is a massive plus.

Tech Stack Overview
Core: Python, TypeScript, React/Next.js.
AI/Data: FastAPI, Pydantic, Vector Databases, Graph Databases, RAG Pipelines.
Ops: Docker, Kubernetes, OpenTelemetry, Keycloak (Auth).