Role Overview

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.

We are seeking a Lead Principal AI Engineer to pioneer this new vector. 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.

Key Responsibilities

AI Architecture & Agentic Frameworks

● 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.

LLM Lifecycle, Fine-Tuning & Evals

● 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.

Technical Leadership & Problem Solving

● 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.

Customer Engagement & Technical Strategy

● 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.

Required 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.

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