Lead Principal AI Engineer

Role Description

We are seeking a Lead Principal AI Engineer who brings a foundational, mathematically grounded understanding of classical Machine Learning, combined with deep hands-on expertise in modern Generative AI, Large Language Models (LLMs), and Agentic Frameworks. In this role, you will serve as both a technical authority and a strategic leader. You will architect end-to-end AI systems--from dataset curation and fine-tuning to building agentic workflows and automated evaluation suites--while working directly with enterprise customers to translate complex business problems into production-grade solutions.

At iBase-t, we are building Frontier--the industry’s first true, purpose-built AI solution for Aerospace & Defense (A&D) manufacturing. A&D manufacturing represents one of the most complex, high-stakes engineering environments in the world, where precision, traceability, and strict compliance are non-negotiable.

You will be a foundational technical architect for Frontier, combining deep, mathematically grounded Machine Learning with cutting-edge Generative AI, LLMs, and autonomous agentic frameworks. In this role, you will bridge the gap between advanced AI research and real-world industrial impact--architecting agentic workflows, domain-specific fine-tuning pipelines, and evaluation suites designed to solve complex manufacturing, quality engineering, and operational challenges while interfacing directly with key customer leadership.

Qualifications

• Education: Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, Electrical Engineering, or a related quantitative discipline.
• US Experience: Minimum 5+ years of professional engineering experience either as ML engineer or AI engineer.
• Core ML First: Strong, foundational understanding of core machine learning principles (optimization, statistical modeling, feature engineering, classic supervised/unsupervised learning, and deep learning architectures) prior to LLMs.
• LLM & Fine-Tuning Mastery: Hands-on experience with dataset curation, parameter-efficient fine-tuning (PEFT), and developing comprehensive model evaluation (evals) methodologies.
• Agentic AI Systems: Proven track record of designing, building, and deploying AI agent architectures, autonomous workflows, and tool integration frameworks.
• Software Engineering: Advanced Python proficiency, with strong software engineering practices (clean code, CI/CD, modular architecture, performance profiling).
• Client-Facing Leadership: Excellent communication and consultative skills with experience interfacing directly with external clients, technical decision-makers, and executive stakeholders.

Requirements

• Design, build, and deploy production-grade agentic frameworks and multi-agent workflows from scratch using clean, scalable Python code.
• Architect custom tool-use protocols, memory systems, and planning mechanisms for autonomous AI agents.
• Bridge classical ML approaches with generative paradigms to build hybrid, resilient systems.
• Drive dataset curation, data synthesis, instruction-tuning, and domain-specific dataset generation pipelines.
• Fine-tune open-source and proprietary models using advanced techniques (e.g., LoRA/QLoRA, PEFT, DPO/RLHF).
• Build rigorous, repeatable evaluation frameworks (e.g., benchmark design, LLM-as-a-judge, custom metric scoring) to ensure reliability, safety, and performance.
• Serve as the principal technical lead across cross-functional engineering efforts, setting coding standards, architecture patterns, and technical strategy.
• Break down complex, ambiguous business challenges into actionable, high-impact machine learning architectures.
• Mentor senior and mid-level engineers in production ML best practices.
• Act as a primary technical lead in client-facing environments, presenting architectural designs, articulating trade-offs, and driving integration with customer engineering teams.
• Gather requirement feedback from stakeholders to directly shape product roadmaps and technical specs.

Preferred Qualifications

• Prior exposure to manufacturing execution systems (MES), PLM/ERP systems, or industrial operations context.
• Experience deploying AI models within secure, air-gapped, or highly compliant environment constraints (e.g., FedRAMP, ITAR).
• Background in vector databases, hybrid search architectures, and complex graph-based RAG.