Lead Principal AI Engineer

Socket.dev; United States; On-site; USD 180,000 - 240,000; Full time

Job summary

Socket.dev is seeking a Lead Principal AI Engineer to architect end-to-end AI systems for Aerospace & Defense applications. You will combine foundational ML theory with hands-on generative AI, LLMs, and agentic frameworks, delivering production-grade solutions for enterprise customers.

You will lead cross-functional teams, define technical strategy, mentor engineers, and translate complex business problems into robust, compliant AI architectures with measurable reliability and performance.

Qualifications

• Master’s or PhD in Computer Science, ML, Data Science, Electrical Engineering or related quantitative discipline.
• 5+ years of professional engineering experience in ML/AI roles in the US.
• Foundational understanding of core ML principles (optimization, statistical modeling, feature engineering, classical learning, deep learning).
• Hands-on experience with dataset curation, PEFT and model evaluation methodologies.
• Experience designing AI agent architectures, autonomous workflows, and tool integration.

Responsibilities

• Design, build and deploy production-grade agentic frameworks and multi-agent workflows using scalable Python code.
• Develop custom tool-use protocols, memory systems, and planning mechanisms for autonomous AI agents.
• Bridge classical ML with generative paradigms to create hybrid systems.

Skills

Foundational ML principles

LLM & Fine-Tuning mastery

Agentic AI systems

Python programming

Client-facing leadership

Education

Master's or PhD in CS/ML/Data Science/EE

Tools

PEFT frameworks (LoRA/QLoRA)

Job description

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