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About This Role
Description
### POSITION DESCRIPTION:
This position is Senior Associate level who be responsible for purchasing materials to support Community, Commercial and Industrial (CC\&I) business projects and for managing all aspects of shipping and delivery. It will support the Sales operations, Product Management, Finance and product management departments in meeting their supply chain needs. This position directly reports to the Senior Manager, Supply Chain Management of Product and Service management team or another senior manager that the President of the Company may designate. This position is based in Teaneck, NJ.
### RESPONSIBILITIES
- Review Bill of materials, vendor quotes and contracts, and up to medium complexity negotiations.
- Place purchasing orders to the vendors and monitor PO status to follow up the projects
- Coordinate the schedule with relevant teams and arrange the project delivery to warehouses and the job site to meet the installation
- Strong understanding of procurement, negotiations and general understanding of shipping procedures and documentation
- Monitor the shipment status and check invoice to request payment to vendors.
- Lead invoice management process and support finance team to ensure timely payments to vendors.
- Update daily PO tracking spreadsheet, including PO, delivery, and Invoice etc.
- Efficient interaction between vendors and internal teams with the highest levels of customer service.
- Adhere to existing supply chain processes and apply both industry and organizational best practices
- Perform other duties and special projects, as assigned.
- Develop reports and dashboards as required.
- Support in developing and improving workflow to improve supply chain operations.
- Identify new vendors as required and manage strong relationships with existing vendors.
### REQUIRED QUALIFICATIONS
- Associate degree (AA)\+ years of experience or bachelor’s degree \+ 3 years of experience in a related field.
- 3\+ years of experience in either procurement and/or supply chain/logistics a must
- Bi\-lingual in English and Korean languages.
- Strong understanding of SAP, especially supply chain modules.
- Possession of a valid passport
- Excellent verbal and written communication skills
- Critical and strategic thinking skills
- Ability to prioritize multiple and simultaneous projects, issues, and activities
- Strong interpersonal, implementation and facilitation skills
- Strong, self\-motivated team player
- Strong Microsoft Office skills, including Outlook, Excel, Word, and PowerPoint
Hanwha Q CELLS America Inc. (“HQCA”) is headquartered in Irvine, CA, and handles sales for the North American region. It is a subsidiary of Hanwha Q CELLS Co., Ltd., one of the world´s largest and most recognized photovoltaic manufacturers for its high\-performance, high\-quality solar cells and modules. It is headquartered in Seoul, South Korea (Global Executive HQ) and Thalheim, Germany (Technology \& Innovation HQ). Through its growing global business network spanning Europe, North America, Asia, South America, Africa, and the Middle East, the company provides excellent services and long\-term partnerships to its customers in the utility, commercial, government, and residential markets. Hanwha Q CELLS is a flagship company of Hanwha Group, a FORTUNE Global 500 firm, and a Top 8 business enterprise in South Korea. HQCA recently acquired Geli, a leading developer of Energy Management System software for energy storage, solar, and other renewable resources.
PHYSICAL, MENTAL \& ENVIRONMENTAL DEMANDS:
To comply with the Rehabilitation Act of 1973 the essential physical, mental and environmental requirements for this job are listed below. These are requirements *normally expected* to perform *regular* job duties. Incumbent must be able to successfully perform all of the functions of the job with or without reasonable accommodation. Mobility
Standing
20% of time
Sitting
70% of time
Walking
10% of time
Strength
Pulling
up to 10 Pounds
Pushing
up to 10 Pounds
Carrying
up to 10 Pounds
Lifting
up to 10 Pounds
Dexterity (F \= Frequently, O \= Occasionally, N \= Never)
Typing
F
Handling
F
Reaching
F Agility (F \= Frequently, O \= Occasionally, N \= Never)
Turning
F
Twisting
F
Bending
O
Crouching
O
Balancing
N
Climbing
N
Crawling
N
Kneeling
N
The salary range is required by the California Pay Transparency Act and may differ depending on the location of those candidates hired nationwide. Actual compensation is influenced by a wide array of factors including but not limited to, skill set, education, licenses and certifications, essential job duties and requirements, and the necessary experience relative to the job’s minimum qualifications.
- This target salary range is for CA positions only and should not be interpreted as an offer of compensation.
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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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Qcells, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Qcells AI Hiring
Qcells has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Teaneck, NJ, US, Santa Clara, CA, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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