Lead AI Engineer, Business Operations (Hybrid or Remote

AFL Telecommunications•Dallas, TX

1d•Hybrid

About The Position

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 opticcableand 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 greatestasset. We hire and traineach 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. 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. TheLead AI Engineeris the first engineering hire on AFL's AI Enablement team, responsible for designing, building, and deploying agentic AI systems 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 helplaythe technical foundation the rest of the team will build on — including model selection and management, deployment posture, orchestration patterns,evaluationandaudit. 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 engineers to stay focused on build work.

Requirements

Bachelor's degree in Computer Science or related field, or equivalent experience
7+ years of software engineering experience with a strong full-stack foundation — backend services, API design, system integration, and data infrastructure
Recent hands-on experience building AI or LLM-backed systems and shipping them to 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 communications skills with both technical and non-technical stakeholders

Nice To Haves

Innovative and tech-savvy, with deep curiosity about emerging AI capabilities and how to apply them
Analytical and detail-oriented, with a strong engineering 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

Responsibilities

Establishes the architectural patterns, evaluation practices, and deployment standards for the team
Makes framework and model recommendations that set the foundation for how the team builds — evaluates orchestration frameworks, selects deployment patterns, trains and fine-tunes models, and determines where managed platforms end and custom build begins
Translates proposed business solutions into technical plans — defines product life cycles, prioritizes the backlog, and breaks initiatives into buildable work
Owns solutions end-to-end: technical planning, architecture, build, deploy, and the monitoring that keeps them honest in production
Builds the monitoring, evaluation, and regression detection systems that keep production agents reliable — including logging, performance benchmarking, and feedback loops that surfacedrift early
Partners with data governance to ensure solutions meet compliance, data quality, and operational standards