Principal Engineer

The Opportunity
We are building a proprietary, industrial-grade AI platform to tackle the "last mile" of Generative AI:  guaranteeing correctness in complex domains.
We are  not  looking for a "Prompt Engineer" or an API integrator. We are seeking a  Research-Grade Engineer  (Masters/PhD preferred) who combines deep theoretical knowledge of NLP with the ability to architect scalable, production-ready systems. You will function as the technical lead of this platform, working directly with the CTO to define the next generation of code intelligence.
The Challenge
Current LLMs are excellent at creativity but struggle with the strict logical consistency required for enterprise software. Your challenge is to bridge this gap. You will design systems that can handle  massive context windows , maintain  semantic integrity  across thousands of files, and deliver  verifiable accuracy .
You will define the methodologies to constrain Generative AI with strict structural rules, answering the hard question:  How do we build a system that possesses the flexibility of a neural network but the reliability of a compiler?
What You Will Do
Architecture Design:  Architect high-reliability inference systems that solve the "hallucination problem" inherent in Large Language Models. You will move beyond out-of-the-box solutions to build defensible, proprietary IP.
Advanced NLP Strategy:  Define the strategy for domain adaptation and long-context reasoning. You will perform first-principles analysis to select the right approach (RAG, Fine-Tuning, or novel methods) based on rigorous benchmarking.
Evaluation & Verification:  Design and build proprietary evaluation frameworks to rigorously measure the performance and safety of our models before they touch client code.
Technical Standards:  Mentor the engineering team on the mathematical underpinnings of Transformer architectures and current SOTA research.
What We Need (Must Haves)
Advanced Degree:  Masters or PhD in Computer Science, AI, or related field (or equivalent top-tier research lab experience).
Advanced LLM Internals:  You understand the specific failure modes of modern architectures regarding  long-context recall ,  reasoning drift , and  hallucination triggers  in complex logic. You don't just fine-tune; you know how to mathematically constrain model outputs to ensure high-fidelity results.
Applied Research:  8+ years of experience, with a track record of taking complex ML research and deploying it into production environments.
Beyond APIs:  Proven expertise in architecting  autonomous agentic systems  that go beyond simple retrieval. You have designed multi-agent orchestrators involving  planning, tool use (function calling), and self-correction loops  to solve multi-step reasoning problems reliably.
Engineering Excellence:  Strong proficiency in Python. You write clean, modular, object-oriented code, not just "notebook scripts."

Preferred Experience
Interest in  Code Generation ,  Program Analysis , or  Semantic Parsing .

Experience with open-source LLM orchestration (Lang Chain, DSPy, Llama Index) but with a critical understanding of their limitations.
Published research or technical blog posts on Applied NLP.