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About This Role
Description
### POSITION DESCRIPTION:
EnFin’s Operations \& Maintenance Analyst focus is remediating orphaned solar customers impacted by out\-of\-business installers; responsible for leading remediation efforts, ensuring timely and effective resolution of customer issues and providing exceptional customer experience. The O\&M Analyst is responsible for developing and managing relationships with alternate installers I.E. O\&M partners to successfully mitigate customers' concerns and achieve Permission to Operate (PTO) from the utility; acts as a liaison between all parties to ensure we meet the regulatory, utility, HOA, AHJ and NEC code requirements for successful project completion.
This position will be based out of one of our offices in Irvine, CA; San Francisco, CA; Santa Clara, CA or Teaneck, NJ. Remote may be considered in exceptional cases.### RESPONSIBILITIES
- Design \& implement an interconnection process where Enfin can take over the processing of the interconnection application on behalf of the installer.
- Map out and deliver interconnection timelines and process for each utility that we have active customers
- Oversee utility relationships to deliver timely PTO's but working collaboratively with Utility representatives
- Develop and maintain productive relationships with our O\&M partners \& regulatory stakeholders for sustainability and successful project/customer mitigation.
- Manage a pipeline for our O\&M partners to ensure projects are completed within the standards of our customers’ expectations; satisfaction and adherence to Service Level Agreements (SLA) and Service Level Objectives (SLO).
- Lead project remediation efforts for orphaned solar customers, ensuring timely and effective resolution of customer concerns.
- Investigate and resolve customer complaints, concerns, and issues related to solar system installation, maintenance, and performance.
- Serve as the primary point of contact for customer\-facing teams, providing regular updates and ensuring transparent communication throughout the remediation process.
- Perform outbound calls to O\&M partners to deal with the complaint process to the customer's desired resolution.
- Develop and maintain databases and spreadsheets.
- Analyze data related to customer issues, remediation efforts, and system performance to identify trends and areas for improvement.
- Ensure compliance with company policies, industry regulations, and relevant laws.
- Follow\-up on inbound calls or emails to positively manage both our customers and business partners’ relationships.
- Schedule regular meetings, discussions, and teleconferences to strengthen the relationship with our O\&M partners.
- Address any customers’ concerns or escalations promptly, professionally, and accurately.
- Raise any business partners’ concerns to the Ops Leadership team promptly.
- Review and analyze our O\&M partners' performance and behavior to address potential risks.
- Identify opportunities for business growth in our Ops Leadership team.
- Review loan documents, PV designs, and installation contracts for accuracy and completeness.
- Perform fraud verification activities to ensure the validity of PII.
### REQUIRED QUALIFICATIONS
- A bachelor’s degree in business or other related field with a minimum of 5\+ years of professional work experience including 4\+ years’ work experience with B2B2C in the solar industry, particularly in O\&M, customer service roles or operations.
- Experience with project management tools and methodologies, engineering, utility requirements, and installation requirements required.
- Solar Industry operations experience in PPA/leases and/or loans, PMP or other project management experience, as well as utility experience; strong knowledge of utility regulations and tariffs.
- Strong analytical and problem\-solving skills.
- Ability to work independently and collaboratively as part of the team.
- Stupendous understanding of solar system design, engineering \& utility requirements, production, installation and maintenance.
- Develop and implement remediation plans for O\&M issues.
- Strong written and oral communications including negotiation skills.
- Perform production and utility bill analysis, ROI investigation.
- Ability to influence and direct third\-party providers to execute and remediate projects in a timely and cost\-effective manner.
- Demonstrated track record of successfully working on multiple projects and activities at one time; must possess the ability to work effectively under pressure, meet strict deadlines, and complete assignments with little oversight.
- Excellent time management and prioritization; enjoy operating in a dynamic and fast\-paced environment.
- Identify areas for process improvement and present solution proposals to Ops Leadership.
- Ability to manage multiple stakeholders and priorities, collaborate with compliance, O\&M providers, and cross functional teams.
- Advanced working skills set with Salesforce, Tableau, and MS Office (specifically Excel and PowerPoint) including advanced Excel formulas (VLOOKUP, if statements, pivot tables).
- Ability to interpret complex regulatory and tariff documents.
- Strong understanding of utility corrections and kickbacks and track record of successful mitigation.
Hanwha Q CELLS America Inc. (“HQCA”) is a Qcells company, one of the world’s largest manufacturers and providers of solar photovoltaic (PV) products and solutions. Headquartered in Irvine, California, HQCA has been rapidly expanding its business in North America through the expansion of products and solutions, including distributed energy solutions, direct\-to\-homeowner solar sales and financing, and EPC services. We provide an opportunity to be part of an exciting and growing world\-class global business in an interesting and expanding industry of the future.
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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