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Principal AI Engineer

Tech M USA
2 days ago
Contract
Remote
$60 - $65 USD hourly
AI Services

Principal AI Engineer to lead the architecture, strategy, and technical evolution of enterprise scale AI platforms and GenAI powered product experiences.

Key Responsibilities

Define and own enterprise AI platform architecture

Lead development of RAG pipelines and AI orchestration frameworks

Build production grade AI systems and proofs of concept

Establish AI quality, evaluation, and governance standards

Drive AI platform design decisions and reusable tooling

Required Technical Skills

Python engineering expertise

LLM applications, vector databases, retrieval infrastructure LangChain, LlamaIndex, semantic search systems

AI evaluations, model lifecycle management, observability

Scalable GenAI systems and MLOps

Required Leadership Skills

12+ years of software engineering experience, with significant tenure at Staff/Principal level.

Drive cross team technical decisions, platform direction, and multi team initiatives (architecture alignment, trade off resolution, execution).

Define and scale AI driven SDLC standards, including tools, guardrails, and automation patterns.

Hands on experience with AI assisted development (e.g., Copilot, Claude Code, Cursor) to deliver production systems; able to articulate workflow impact and guardrails.

Strong experience designing and evolving large scale distributed systems (APIs, data pipelines, event driven systems, high scale platforms).

Proven cross org leadership‿driving architecture, standards, and platform initiatives across multiple teams.

Elevate engineering quality through code reviews, architecture reviews, and mentoring senior engineers.

Strong engineering fundamentals in code quality, testing, CI/CD, and observability while maintaining delivery velocity.

Effective communicator with the ability to influence stakeholders and drive alignment without formal authority.

Experience collaborating across product, data, security, and infrastructure teams.

Track record of documenting and scaling architectural decisions and organizational learnings.