I
Principal AI Engineer
Infogain
1 hour ago
Full-time
On-site
Plano, Texas, United States
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 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
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:
Experience building custom inference pipelines, optimizing vector search algorithms, or designing complex retrieval systems. 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 (LangChain, DSPy, LlamaIndex) but with a critical understanding of their limitations. Published research or technical blog posts on Applied NLP.
#J-18808-Ljbffr
Research-Grade Engineer
(Masters/PhD preferred) who combines deep theoretical knowledge of NLP with the ability to architect scalable, production-ready systems. 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
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:
Experience building custom inference pipelines, optimizing vector search algorithms, or designing complex retrieval systems. 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 (LangChain, DSPy, LlamaIndex) but with a critical understanding of their limitations. Published research or technical blog posts on Applied NLP.
#J-18808-Ljbffr