Senior AI Engineering Lead

Job Description

• Position Title: Senior AI Engineering Lead
• Industry Group: Corporate
• Reporting Manager Title: VP Technology 

Travel Required? No

Direct Reports? No

Position Summary Details

We are seeking a Senior AI Engineering Lead to pioneer our next generation of intelligent systems. This is a unique, in office role for a Ph.D.-level expert to merge cutting-edge research with the architectural vision and engineering leadership required to deploy complex, frontier AI models at a massive scale. The ideal candidate will set the strategic direction for AI/ML/GenAI across the enterprise while personally driving the technical design and optimization of our most advanced and challenging models.

Essential Functions

I. Frontier AI Research & Model Engineering

• Pioneering Research: Lead fundamental and applied research in areas like Generative AI, Multi-modal Foundation Models, and advanced agentic systems.
• Model Optimization: Drive the research and implementation of advanced techniques for Model Alignment (e.g., RLHF, DPO), Quantization, and Distillation to significantly improve the performance, cost, and latency of large-scale models for real-time inference.
• Data-Centric AI: Design and implement innovative strategies for data curation, synthetic data generation, and active learning to continuously enhance model performance with minimal human labeling.
• AI Safety and Trust: Establish methods for AI Guardrails, Adversarial Robustness, and Bias Mitigation to ensure all production systems adhere to the highest ethical and regulatory standards.

II. AI Architecture & Deployment Leadership 

• System Architecture: Define the technical roadmap and architect end-to-end solutions for deploying frontier AI, including Large Language Models (LLMs), RAG systems with Vector Databases, and Multi-Agent Frameworks (e.g., LangChain, CrewAI).
• MLOps at Scale: Architect and govern a next-generation MLOps platform focused on automated model training, continuous delivery (CI/CD), version control, and comprehensive model monitoring for distributed deployments.
• Compute Optimization: Lead the evaluation of specialized AI infrastructure (e.g., NVIDIA GPUs, TPUs) and design the distributed computing strategy for efficiently training and serving multi-billion parameter models.
• Technical Authority: Serve as the final technical authority, conducting deep-dive architectural reviews and providing technical sign-off for all major AI initiatives across the company.

III. Strategic Management & Mentorship 

• Strategic Direction: Partner with senior leadership to define the long-term AI strategy, research, translating complex research breakthroughs into measurable business value.
• Technical Leadership: Lead and mentor a team of Senior AI Engineers, fostering a culture of rigorous scientific inquiry, engineering excellence, and rapid innovation.
• Cross-Functional Collaboration: Act as the technical bridge between Data & AI Engineering, Product Management, and business stakeholders, clearly communicating complex technical concepts to drive alignment.

Scope Factors (i.e. Revenue Scope, Assets Scope, Budget Scope, Profit/Loss Scope, Supervisory Scope)

· Collaborate with vendors, tech platform SME, external teams and direct/guiding internal resources for delivering AI solutions to meet business strategy.

Qualifications – Education & Experience

· Education & Experience:

o Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field with

2+ years of post-doctoral professional experience and 6+ years of total experience

o OR Master’s degree in a relevant field with 8+ years of progressively responsible professional experience.

• Foundation Model Expertise: Deep theoretical and hands-on expertise in the architecture, training, fine-tuning, and deployment of Large Language Models (LLMs) and/or Multi-modal Models.
• Programming & Systems: Expert-level proficiency in Python, deep learning frameworks (PyTorch, JAX), and high-performance, distributed computing systems (Kubernetes, Ray, MPI).

Qualifications – Other Skills, Abilities & Knowledge

• Experience with Neuro-Symbolic AI, Reinforcement Learning (RL), or Causal Inference applied to real-world systems.
• A proven track record of leading the development of a production-grade, multi-agent AI system or autonomous decision-making engine.
• Deep practical knowledge of AI hardware architecture (GPU/TPU) and its impact on large-scale model performance.
• Experience in an industry with a strong focus on compliance and Responsible AI governance frameworks.
• A history of work in model efficiency, distributed training, or novel neural network architectures.
• Foundational knowledge of cyber security and data governance

Qualifications – License(s) & Language(s)

N/A

Working Environment and Travel Requirements

Atlanta Metro area. Minimal travel required, not expected to be more than 20%

• Benefit Information:
• ABM offers a comprehensive benefits package. For information about ABM’s benefits, visit ABM Team Member Benefits | Staff & Management 

Recruiting Flyer - Staff & Mgmt