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About This Role
Amphenol Industrial Operations
Reports To: Controller, AIO
The AI Solutions Manager will lead the development and deployment of enterprise artificial intelligence solutions across all Amphenol Industrial Operations (AIO) functions. This position serves as AIO's central AI resource, partnering with engineering, operations, manufacturing, quality, sales, marketing, finance, customer service, and information technology to identify, prioritize, and implement high\-value AI initiatives. The manager will be responsible for developing AI systems that improve productivity, automate business processes, enhance decision\-making, and drive business growth. This role requires strong technical expertise in artificial intelligence, machine learning, large language models (LLMs), intelligent automation, and data\-driven systems, combined with strong business acumen to align AI investments with organizational objectives and measurable business outcomes.
The individual will operate as a working manager, personally contributing to the design, development, deployment, and support of AI solutions while simultaneously leading projects, coordinating internal resources, and managing external technology partners. This role will evaluate and determine the most effective delivery approach for each AI initiative, leveraging internal resources, external partners, outsourced development, or hybrid models as appropriate to optimize speed, quality, scalability, and return on investment.
Reporting through the Information Technology organization, the manager will establish and grow AIO's AI capabilities, define enterprise AI standards and governance practices, and lead the development of future Amphenol\-owned AI systems and platforms.
Essential Duties \& Responsibilities:
AI/ML Architecture \& Development (Initial \+ Future Responsibility)
- Serve as the central AI/ML technical resource for all AIO functions and business units.
- Collaborate with technology partners to architect enterprise LLM platform.
- Ensure AI capabilities integrate with engineering, sales, marketing, operations, manufacturing, quality, finance, customer service, IT, and customer\-facing systems.
- Participate in cross\-functional design sessions, capturing enterprise\-wide requirements.
- Develop scalable APIs and AI services enabling LLM usage across multiple business units.
- Build internal capability for future LLM fine\-tuning, training, and lifecycle management.
- Help define, support, and execute the division AI roadmap aligned with business objectives, operational efficiency, revenue growth, and measurable return on investment.
- Evaluate AI opportunities and determine the optimal development strategy using internal resources, external partners, outsourced development teams, or hybrid approaches.
- Define enterprise standards, governance, architecture, and best practices for AI systems deployment and support.
Enterprise Automation Use Cases
- Automate engineering outputs including BOMs, drawings, ECNs, FAIs, validation reports, design tasks and processes including FMEAs.
- Develop AI\-driven product configurators supporting sales, marketing, distributors, and customers.
- Enable automated generation of datasheets, catalogs, application notes, and technical marketing content.
- Create intelligent website tools that guide customers through product selection with real\-time technical outputs.
- Automate manufacturing process documentation including routings, work instructions, and validation records.
- Develop conversational AI tools for CRM platforms, distributor portals, and customer service workflows.
Sales, Marketing \& Website AI Configurator Development
- Build AI configurators that generate part numbers, compatibility checks, quotes, and selection recommendations.
- Integrate configurators into CRM, ERP, digital catalog systems, and public\-facing websites.
- Enable automated production of customer\-ready outputs including drawings, BOMs, and datasheets.
- Collaborate with marketing to ensure accuracy, branding, and regulatory compliance.
Cross\-Functional Collaboration
- Serve as AIO's primary AI resource supporting all functional groups including engineering, operations, manufacturing, quality, supply chain, sales, marketing, finance, customer service, and IT.
- Partner with business leaders to identify high\-value opportunities where AI can improve efficiency, quality, revenue growth, customer experience, and decision making.
- Work closely with internal teams and external technology partners to ensure successful implementation of AI initiatives.
- Provide leadership, mentoring, training, documentation, rollout support, and change management support during enterprise AI adoption.
- Function as a working manager by balancing hands\-on AI system development with project leadership, resource planning, and vendor coordination.
Data Engineering \& Governance
- Develop datasets for enterprise LLM training including engineering data, sales inputs, marketing content, and manufacturing information.
- Build RAG pipelines ensuring AI systems reference validated engineering, commercial, manufacturing, and operational data.
- Establish data governance and security frameworks to protect IP and ensure compliance.
- Work with IT to ensure AI systems follow enterprise security, access control, data retention, and system support requirements.
Continuous Improvement \& Innovation
- Develop and maintain an enterprise AI roadmap aligned with AIO strategic objectives.
- Continuously evaluate whether future AI initiatives should be internally developed, outsourced, partnered, or acquired based on business requirements and resource availability.
- Establish metrics to measure AI effectiveness, business value, user adoption, cost, reliability, and return on investment.
Qualifications:
Education \& Experience Requirements
- Bachelor's or Master's degree in Engineering, Computer Science, Data Science, Business Technology, or related discipline.
- Experience building software systems at scale and improving data architecture including developing and integrating AI tools across engineering, sales, marketing, operations, manufacturing, quality, finance, customer service, or IT.
- Experience working with technology partners on AI development programs.
- Demonstrated experience leading enterprise\-wide technology or AI initiatives.
- Experience managing technology projects and coordinating internal and external development resources.
- Experience evaluating build\-versus\-buy decisions and outsourced software development partnerships.
Technical Skills
- Advanced expertise in artificial intelligence, machine learning, large language models (LLMs), intelligent agents, autonomous workflows, MLOps, vector databases, RAG systems, and enterprise AI architecture.
- Demonstrated ability to design, develop, deploy, and support production AI systems delivering measurable business outcomes.
- Experience with LLM fine\-tuning, prompt engineering, model performance evaluation, monitoring, reliability, latency, cost control, and AI quality/safety assessment.
Business \& Leadership Skills
- Strong business acumen with ability to align AI investments to strategic business objectives and financial returns.
- Ability to translate business requirements into effective AI solutions and implementation roadmaps.
- Proven working manager capable of balancing hands\-on technical contributions with project leadership responsibilities.
- Strong vendor management and outsourcing assessment capabilities.
- Ability to evaluate costs, risks, timelines, capabilities, and business impacts when determining AI implementation approaches.
- Excellent communication and collaboration skills across executive leadership, technical teams, and functional business groups.
Soft Skills
- Ability to collaborate across global teams and business units.
- Strong communication skills for technical and non\-technical audiences.
- Self\-directed with ability to lead enterprise\-wide AI initiatives through influence, hands\-on contribution, and structured execution.
Position must be located within the United States. Hybrid or remote work arrangements may be considered based on business needs. Domestic and international travel will be required.
Company Introduction:
Amphenol Industrial Operations, headquartered in Endicott, New York, with global manufacturing, design, sales, and marketing locations, specializes in delivering a comprehensive range of high\-reliability power and signal connectors along with interconnection systems designed specifically for harsh environments and high performance. Our solutions cater to diverse, high\-growth industrial sectors such as alternative energy, energy storage, hyperscale datacom, telecom, rail and mass transit, electric and hybrid vehicles, transportation, factory automation, robotics, and heavy equipment.
Our product portfolio addresses modern power and signal requirements and emerging system technology needs encompassing high\-amperage power interconnects utilizing proprietary RADSOK® contact technology, ruggedized circular, rectangular interconnects and board level contacts (GT, AC, PT, Amphe\-Lite, Radsert, PowerBlok, SurLok Plus, ePower, UPC, H4 Plus, and more), advanced power distribution including value\-add box builds, custom\-engineered cable assemblies and overmolded harnesses. With a dedicated team of over 2500 skilled staff operating across a growing international footprint—including eight key manufacturing sites in North America, Asia\-Pacific, Europe, and the Middle East providing localization for our customer base. Amphenol Industrial Operations consistently meets the highest global standards of quality, product performance, design capabilities, and regionalized technical and sales support.
Amphenol Industrial Operations proudly operates as a business unit of Amphenol Corporation (NYSE: APH), a global leader in the electronics revolution, headquartered in Wallingford, Connecticut.
www.amphenol\-industrial.com
Physical/Environmental Requirements:
The physical requirements described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. While performing the duties of this job, the employee is regularly required to talk, hear, sit, stand; walk; and use hands to finger, handle, or feel. Must be able to sit and/or stand for long periods of time.
Salary Context
This $130K-$150K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($140K) sits 35% below the category median. Disclosed range: $130K to $150K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Amphenol AI Hiring
Amphenol has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $150K - $150K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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