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### Overview
As the Senior Director, AI Services and Software Product Management, you will be guided by the vice president of Product Management and Production and responsible for understanding core technology trends and applying them to product strategy across hardware, device firmware, API, security, cloud services, software applications, and developing an ecosystem of third\-party programs.
### Responsibilities
- Creation of new revenue generating professional services and software solutions leveraging AI
- Desire to drive transformation within the organization and create change
- Responsible for the entire service life cycle from concept to end of life.
- Strategy and analysis activities in coordination with SIICA strategy and objectives.
- Develop a team of managers to grow subordinate team members
- Work closely with sales leadership to build a revenue generating professional services organization
- Direct efforts of competitive analysis to keep abreast of current market conditions and trends.
- Reviews the development and implementation of activities related to pricing, programs, sell\-through forecasting, collateral materials development, public relations interface, and general marketing support as required by the product segment.
- Based on an in\-depth survey of market needs, develop product strategy recommendations necessary for inclusion in future products and present them as a proposal to management.
- Responsible for delivering product planning unit forecast plans along with sell\-through strategy.
- Assists in the preparation/presentation of special major account presentations.
- Reviews/coordinates major account support needs (e.g., product tests, product delivery coordination, etc.).
- Assists the product/sales training and Support departments with required component development.
- Worked collaboratively with other Sr. Product Managers and Product Managers, under the direction of the VP of Product Management and Production, on special projects and R\&D to develop coordinated full\-line strategy proposals and presentations.
- Reviews engineering designs, user stories, and specifications and provides feedback to Sharp Japan for new services and applications.
### Position Requirements
- Prior experience in a leadership role in product management, with a minimum ten years experience in product management and/or partner programs, preferably in the office equipment or IT industry.
- Demonstrated understanding of product lifecycle management
- Understanding of business process requirements and integration between SaaS offerings and IT/Operations infrastructure, including ERP and Dispatch/Ticketing systems.
- Experience developing 3rd party relationships and program
- Ability to work with developers, understand high level design architecture, including AI and ML concepts
- Working knowledge of office technology enabling software applications, including Document Management, Device Management, Cost Accounting, Security.
- Strong knowledge of Computer Hardware, Software, and Networks.
- Experience working with Agile and Kanban development styles
- Advanced experience with product strategy formulation and market trends and analysis.
- Bachelor's degree in business, Marketing, Engineering, or Computer Science or related business experience.
- Frequently requires early morning or evening video calls due to timezone differences with Japan, Europe, and other locations
- Up to 50% travel primarily in the US.
### About Sharp Imaging and Information Company of America (SIICA)
Sharp Imaging and Information Company of America (SIICA) is a division of Sharp Electronics Corporation, the U.S. subsidiary of Japan's Sharp Corporation, a global technology company which has been named to Fortune magazine's World's Most Admired Company List. Sharp strives to help businesses achieve Simply Smarter work by helping companies manage workflow efficiently, create immersive and engaging environments, and increase productivity. SIICA offers a full suite of secure printer and copier solutions, professional and commercial visual displays and projectors, software management and productivity software and markets durable Dynabook laptops. As a total solutions provider, Sharp has a reputation for innovation, quality, reliability, and industry\-leading customer support expertise.
### Employee perks
- Comprehensive, family\-friendly healthcare plans (medical, dental, vision).
- 401k retirement plan with a competitive match and plenty of financial support tools.
- Employee Assistance Plan to care for you and your family's mental and behavioral health, balance, and support. Financial protection for you and your family (life insurance and disability insurance)
- Rewarding and holistic wellness program.
- Training, professional development, and mentorship
- Full suite of voluntary insurance benefits for financial planning (auto, home, ID protection and legal)
- Dynamic culture eager to innovate, enhance diversity, and work smarter.
##### *Sharp Electronics Corporation is an equal opportunity employer – minority – female – disability \- veteran.*
##### *As part of our recruitment process, Sharp Electronics may use automated tools, including artificial intelligence, to assist in the recruiting process. These tools are used to support, and not replace, human decision\-making. We are committed to fair and equitable hiring practices, and all hiring decisions are made or reviewed by qualified personnel.*
##### *All applicants must be authorized to work in the US without sponsorship. All applications must include compensation expectations in order to be considered. Local candidates only, please.*
Salary Context
This $188K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At SHARP CORPORATION, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. Disclosed range: $188K to $240K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
SHARP CORPORATION AI Hiring
SHARP CORPORATION has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Montvale, NJ, US. Compensation range: $240K - $240K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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