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About This Role
Location:
Raleigh, NC
Department:
Engineering
Posted:
6/2/2026
Location Name:
Raleigh
Wage:
Depends on Experience
Amphenol Communications Solutions (ACS), a division of Amphenol Corporation, is a world leader in interconnect solutions for Communications, Mobile, RF, Optics, and Commercial electronics markets. Amphenol Corporation is one of the world’s largest designers and manufacturers of electrical, electronic and fiber optic connectors and interconnect systems, antennas, sensors and sensor\-based products and coaxial and high\-speed specialty cable. ACS has an expansive global presence in research and development, manufacturing, and sales. We design and manufacture a wide range of innovative connectors as well as cable assemblies for diverse applications including server, storage, data center, mobile, RF, networking, industrial, business equipment, and automotive.
Title: AI Lead
Location: Raleigh, NC
The AI Lead will own the AI Solutions \& Automation pillar of the CBS Digital Transformation \& AI Enablement organization. This role is responsible for translating high\-value business opportunities into practical, scalable AI solutions, intelligent automation, copilots, agents, and workflow\-enabled capabilities across engineering, operations, quality, supply chain, finance, and commercial functions.
The AI Lead will partner closely with the Information Systems Lead, Transformation PMO, corporate IT/security, implementation partners, and Federated Center of Excellence functional champions. The role is focused on AI solution strategy, solution design, automation enablement, governance, deployment, adoption, and value realization. Data platform architecture, source\-system onboarding, and core data pipelines will be owned by the IS/Data team, with the AI Lead responsible for ensuring AI solutions are built on trusted, governed, and reusable data assets.
RESPONSIBILITIES:
- Lead the CBS AI Solutions \& Automation roadmap, including practical AI use cases, copilots, agents, intelligent applications, and workflow\-enabled business solutions.
- Partner with the Transformation PMO and Federated Center of Excellence to define, prioritize, and manage the AI/automation use\-case pipeline based on business value, feasibility, data readiness, adoption risk, and governance requirements.
- Translate business problems into scalable AI solution concepts, including requirements, solution patterns, success measures, adoption approach, and sustainment expectations.
- Lead the design and deployment of AI\-enabled solutions such as knowledge assistants, engineering productivity tools, document summarization, classification, risk summaries, anomaly detection support, and agentic workflows.
- Partner with the IS Lead and Data Engineer to assess data readiness, define required data products, and ensure AI solutions are connected to trusted, governed, and secure data sources.
- Define reusable AI solution patterns, prompt/agent design standards, solution templates, documentation practices, and deployment playbooks to avoid one\-off experimentation.
- Evaluate and guide the use of approved AI and automation platforms such as ChatGPT Enterprise, Microsoft Copilot, Claude Code, Azure AI, Microsoft Fabric, Databricks AI, Power Platform, and related tools.
- Partner with corporate IT, cybersecurity, legal/compliance, and data owners to establish responsible AI guardrails, IP protection, access controls, model usage standards, and approval processes.
- Lead or coordinate external partners and vendors supporting AI solution design, automation delivery, AI governance, and technical implementation workstreams.
- Support pilots, user testing, training, deployment, and adoption in partnership with functional leaders, the Transformation PMO, and Federated Center of Excellence champions.
- Track adoption, utilization, productivity impact, and business value of deployed AI solutions in partnership with the Transformation PMO and Finance.
- Build capability within the organization by coaching business users, functional champions, and technical team members on effective and responsible AI usage.
- Travel as required to support global operations, deployment activities, and business transformation initiatives.
- Perform other duties and responsibilities as required to support the growth and success of the business.
QUALIFICATIONS:
- BS in Information Systems, Computer Science, Engineering, Data Science, Business Analytics, or related field; advanced degree preferred but not required.
- 7\-10\+ years of experience in digital transformation, applied AI, automation, analytics, enterprise systems, or technology\-enabled business transformation, preferably within a manufacturing or engineering environment.
- Demonstrated experience translating business problems into practical AI, automation, analytics, or digital solutions that are deployed into real workflows.
- Working knowledge of generative AI, large language models, copilots, agents, prompt/agent design, retrieval augmented generation (RAG), enterprise search, document intelligence, model evaluation, and AI governance concepts.
- Experience with one or more platforms such as Azure AI, ChatGPT Enterprise, Microsoft Copilot, Claude, Microsoft Fabric, Databricks, Power Platform, Logic Apps, or comparable enterprise AI/automation technologies.
- Strong understanding of data readiness, data governance, secure access, and the dependency of AI solutions on trusted data foundations.
- Experience with workflow automation, API\-enabled solutions, low\-code/no\-code platforms, or business\-process automation strongly preferred.
- Strong business process orientation, with the ability to understand engineering, operations, quality, supply chain, finance, and commercial workflows.
- Experience developing governance, adoption, training, and value\-tracking practices for new digital capabilities.
- Strong leadership, communication, stakeholder management, and change\-management skills with the ability to influence across functions without direct authority.
- Experience managing implementation partners, vendors, and cross\-functional technical teams preferred.
- Travel required as needed to support global sites and deployment activities.
Amphenol Corporation is proud of our reputation as an excellent employer. Our focus is to provide the highest level of support and responsiveness to both our employees and our customers, the world's largest technology companies. Amphenol Corporation offers the opportunity for career growth within a global organization. We believe that Amphenol Corporation is unique in that every employee, regardless of his or her position, has the ability to positively impact the business.
Amphenol is an “Equal Opportunity Employer” \- Minority/Female/Disabled/Veteran/Sexual Orientation/Gender Identity/National Origin. For additional company information please visit our website at https://www.amphenol\-cs.com/
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 Amphenol, 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.
Amphenol AI Hiring
Amphenol has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Nashua, NH, US, Raleigh, NC, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
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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