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About This Role
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.
What We Offer:
- Flexible time off policy
- 401K Company match (up to4% —dollar for dollar)
- Professional development, training, and tuition reimbursement programs
- Excellent medical, dental, vision, and life insurance policy options
- Opportunities for career advancement with anindustry leadingcompany!
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.
Responsibilities:
Key responsibilities/essential functions include:
Architecture \& Technical Foundation
- Establishesthe architecturalpatterns, 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, anddetermineswhere managed platforms end and custom build begins
Solution Design \& Delivery
- 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
Production Reliability
- Buildsthe monitoring, evaluation, and regression detection systems that keep production agents reliable — including logging, performance benchmarking, and feedback loopsthat surfacedrift 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 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
Qualifications:
- Bachelor's degree in Computer Scienceor 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
- Proficiencyin Python; experience with cloud platforms
- Experience mentoring engineers and setting technical direction across multiple initiatives
- Strong communicationskills with both technical and non\-technical stakeholders
Working Conditions:
- Environment: Remote work environment (US\-based).
- Travel: Occasional travel (domestic) as needed.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At AFL, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
AFL AI Hiring
AFL has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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