Interested in this AI/ML Engineer role at UtiliSave, LLC?
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About This Role
About UtiliSave
Join a team that has delivered over $700 million in refunds and savings to clients\-and help us reach $1 billion. For over three decades, UtiliSave has been a leader in utility bill auditing and cost recovery. As our company rapidly expands its investment in AI and cloud technology, we're seeking an AI Platform Support Engineer to help build, maintain, and grow our production AI platform.
About the Role
This is a ground\-floor opportunity to grow alongside a company in the middle of a major AI and technology transformation. Reporting directly to the AI Lead, you will work hands\-on with our AI Assistant Platform, which leverages large language models, retrieval\-augmented generation (RAG), and embedding\-based search to serve auditor and operational workflows. Our current production stack runs on AWS (Amazon Bedrock, Amazon Q Business, AWS Textract, and S3\), but we value strong fundamentals in AI engineering over exclusive expertise in any single cloud.
This is a full\-time position with exciting growth potential. The ideal candidate is an energetic and curious team player, comfortable learning new technologies quickly, and brings initiative to a fast\-moving environment. This role offers a clear path to grow into senior AI engineering or platform architect responsibilities as we scale..
Key Responsibilities
- Support the day\-to\-day operation and maintenance of the AI Assistant Platform, including monitoring system health, triaging issues, and coordinating fixes with the development team.
- Build and maintain RAG pipelines\-chunking, embedding generation, vector indexing, retrieval tuning, and evaluation\-to keep the knowledge base accurate and relevant.
- Configure and refine LLM agent behavior, including instruction tuning, tool/Action Group testing, and prompt engineering to improve answer quality for auditor queries.
- Assist with data pipeline tasks including metadata extraction, JSON generation, SQL loading, and semantic indexing.
- Coordinate and execute testing cycles for AI features, collect structured feedback from auditors, and track resolution in JIRA.
- Support document extraction workflows for utility bill processing, validating accuracy and expanding template coverage to new formats.
- Evaluate, pilot, and recommend emerging AI tools and capabilities (e.g., Claude Code, Claude for Excel, Cursor IDE, Amazon Bedrock Agent Core) for adoption across the organization.
- Maintain technical documentation for AI platform components, data flows, agent configurations, and operational runbooks.
- Assist with cloud storage organization, data source management, and cost optimization across AI services.
- Support future integration between the AI platform and CRM by helping define data exchange interfaces and testing data flows.
- Collaborate cross\-functionally with auditors, developers, and leadership to translate business needs into AI platform improvements.
How to Apply
Email your resume and a short note describing:* Your experience with AI tools, cloud platforms, or data pipelines.
- A technical challenge you solved and what you learned from it.
- What excites you about working on a production AI platform.
Send materials to [email protected] with the subject line “AI Platform Support Engineer.”
Requirements Qualifications
- 2–4 years of experience in a technical role involving AI/ML tools, cloud platforms, data engineering, or software development support.
- Solid working knowledge of modern AI and LLM concepts, including prompt engineering, retrieval\-augmented generation (RAG), embeddings, vector databases, and agent\-based architectures.
- Demonstrated experience building or operating RAG or embedding\-based systems (from prototype to production).
- Hands\-on experience with at least one major cloud platform (AWS, Azure, or GCP). Experience with AWS AI services (Bedrock, Q Business, Textract, S3\) is a strong plus, but equivalent experience on other clouds is welcome.
- Proficiency in Python (preferred) or JavaScript for scripting, data processing, and automation.
- Experience with SQL databases for querying, loading data, and basic schema design.
- Familiarity with AI\-assisted development tools such as Cursor IDE, Claude Code, or GitHub Copilot.
- Familiarity with work\-tracking tools such as JIRA, Azure DevOps, or similar.
- Strong communication skills, with the ability to document technical processes and communicate status to both technical and non\-technical stakeholders.
- Self\-starter mentality, strong curiosity about AI advancements, and a willingness to learn quickly.
- High attention to detail when working with data pipelines, configurations, and testing workflows.
- Exposure to document extraction/OCR tools is a plus.
- Bachelor's degree preferred but not required; equivalent hands\-on experience accepted.
Benefits Compensation and Benefits
- Competitive base salary (starting target range: $65,000–$80,000\) plus performance\-based bonus.
- Comprehensive benefits, including health, dental, and vision insurance, 401(k) with company match.
- Generous PTO plus 11 paid holidays.
- Job Type: Fully remote role. Work Hours 9–6 PM EST, must be based in US in a state with no more than 1 hour time difference from EST. Must be authorized to permanently work in US, with no need for visa support.
Salary Context
This $65K-$80K range is in the lower quartile 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 UtiliSave, LLC, 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 ($72K) sits 66% below the category median. Disclosed range: $65K to $80K.
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.
UtiliSave, LLC AI Hiring
UtiliSave, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $80K - $80K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 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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