Lead AI Engineer, Business Operations (Hybrid or Remote)

Description AFL manufactures industry-leading fiber optic cable, connectivity and accessories and provides engineering and installation services for some of the largest telecom customers in the world. Our company was founded in 1984 with a single fiber optic cable and today, we manufacture thousands of products, generate an excess of $2B in revenue, and employ approximately 11,000 associates worldwide. At AFL, we recognize that our employees are our greatest asset. We hire and train each individual, investing in them to ensure success in their careers. With a commitment to professional development and growth, let us connect you to your next career opportunity. What We Offer: Flexible time off policy 401K Company match (up to 4% — dollar for dollar) Professional development, training, and tuition reimbursement programs Excellent medical, dental, vision, and life insurance policy options Opportunities for career advancement with an industry leading company! We are seeking a Lead AI Engineer to join our Business Operations team. This position may be able to work remotely from anywhere within the United States. The Lead AI Engineer is a senior technical leader on AFL's AI Enablement team, responsible for defining the architecture, standards, and delivery approach for agentic AI solutions that automate the operational backbone of the business through workflow orchestration, model adaptation, and analytics. Working directly with the AI Enablement Manager, the Lead AI Engineer will establish the technical foundation the team builds on, including model selection and management, deployment posture, orchestration patterns, evaluation and audit. As the team grows, an AI Product Manager and AI Operations Specialists will join to take on intake, sequencing, stakeholder coordination, and product ownership of deployed solutions, allowing the team to stay focused on design, delivery, and maintenance of future solutions. Responsibilities: Key responsibilities/essential functions include: Architecture & Technical Foundation Establishes the architectural patterns, evaluation practices, and deployment standards for the team Makes framework and model recommendations that set the foundation for the team. Will evaluate orchestration frameworks, select deployment patterns, train and fine-tune models, and determine where managed platforms end and custom build begins Solution Design & Delivery Collaborates with the AI Enablement Manager and business unit to optimize operational processes, owning the infrastructure architecture and translating strategic direction into technical plans, product life cycles, a prioritized backlog, and buildable work Owns solutions end-to-end: technical planning, architecture, build, and deployment, then continuously monitors, iterates, and improves them to keep them reliable in production Production Reliability Builds and continuously maintains the monitoring, evaluation, and regression detection systems that keep production agents reliable over time, including logging, performance benchmarking, and feedback loops that surface drift early Governance & Collaboration Partners with data governance to ensure solutions meet compliance, data quality, and operational standards Personal Qualities: Innovative and tech-savvy, with deep curiosity about emerging AI capabilities and how to apply them Analytical and detail-oriented, with a strong architectural mindset Collaborative and communicative, able to translate complex technical concepts for non-technical stakeholders Self-directed and accountable, able to set technical direction and drive execution independently Qualifications: Bachelor's degree in Computer Science or related field, or equivalent experience 5+ years of software engineering and architecture experience with a strong full-stack foundation — backend services, API design, system integration, and data infrastructure Recent hands-on experience architecting and delivering AI or LLM-backed systems in production Experience architecting solutions from scratch and owning them through deployment, observability, testing, and ongoing reliability Experience with AI development practices — model selection, fine-tuning, prompt engineering, evaluation frameworks, and understanding when each approach is the right fit Proficiency in Python; experience with cloud platforms Experience mentoring engineers and setting technical direction across multiple initiatives Strong communication skills with both technical and non-technical stakeholders Working Conditions: Environment: Remote work environment (US-based). Travel: Occasional travel (domestic) as needed.