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About This Role
Job Description for Agentic AI Intern at CustomerInsights.AI, Inc (CIAI)
An Agentic AI Intern at CIAI will have the opportunity to apply artificial intelligence and machine learning engineering skills to real decisions impacting healthcare challenges today. The Agentic AI Intern will be responsible for developing, deploying, and optimizing AI systems that can transform raw data into intelligent insights and automated solutions to improve business outcomes and ultimately help deliver better patient care. As an intern, you would have the opportunity to work with cutting\-edge AI technologies, build and fine\-tune AI models, and help design intelligent systems that can learn, adapt, and make autonomous decisions. Once AI solutions have been developed, the AI Intern will demonstrate and communicate the capabilities and impact of these systems to other project stakeholders.
Duties and responsibilities of the job:
Agentic AI Interns often make recommendations about AI strategies, architecture, model selection, and deployment strategies that enable companies to leverage artificial intelligence for competitive advantage and operational efficiency. As an intern, he/she will have the opportunity to refine their AI engineering skills and translate complex AI concepts into implementations that can be understood and utilized at multiple levels within the organization from the Executive team (C\-Suite) who examine AI strategy at a high level, to the managers and directors who will implement AI\-driven processes.
The AI intern will be responsible for:
- Emerging AI Techniques Research \- Track new developments in agentic AI (frameworks, benchmarks, orchestration methods) and summarize which are relevant and practical for ciATHENA's roadmap.
- Competitor Benchmarking \- Research leading agentic AI products in the market and identify capabilities they offer that ciATHENA doesn't. Deliver a clear, concise comparison report for leadership review.
- Gap Analysis for ciATHENA \- Evaluate ciATHENA's current capabilities against client and market needs. Identify and prioritize gaps based on real business impact, distinguishing must\-haves from nice\-to\-haves.
- Build vs. Buy Recommendations \- For each identified gap, assess whether it's more efficient to build the capability in\-house or adopt an existing framework/tool. Provide a simple cost\-benefit view to support decision\-making.
- Cross\-Client Project Review \- Analyze ongoing projects across clients (data warehousing, reporting, analytics) to identify the most time\-consuming, repetitive tasks. Recommend which of these are strong candidates for automation through ciATHENA.
- Stress\-Testing ciATHENA \- Proactively test ciATHENA with ambiguous instructions, edge cases, and messy data to uncover failure points and inconsistencies — surfacing issues before clients do.
- Agent evaluation \& observability harness \- Score Athena's LLM calls (NL→SQL accuracy, hallucination, latency, cost). The backbone that makes every other experiment provable.
- Self\-healing data pipelines \- Agents that detect and fix schema drift / data\-quality issues autonomously, with guardrails.
- Text\-to\-SQL \& semantic\-layer robustness \- schema\-linking, join\-grain\-trap detection, semantic caching, auto\-repair the highest\-leverage reliability work.
Job qualifications and requirements:
A degree in the following subjects is beneficial in developing a career in AI:
· Data Science and Machine Learning
· Computer Science
· Statistics
· Analytics
· Mathematics
· Life Science experience is helpful – but not required
Technical skills:
· Hands\-on experience in developing and fine\-tuning generative AI models (LLMs, diffusion models, etc.)
· Experience in designing and implementing autonomous AI agents capable of decision\-making, planning, and adapting to dynamic environments
· Knowledge of MLOps practices and AI model deployment strategies
· Experience with cloud AI platforms (AWS SageMaker, Google AI Platform, Azure ML, etc.)
· Strong programming skills in Python, with experience in AI/ML frameworks
· Understanding of transformer architectures, attention mechanisms, and modern AI model architectures
· Knowledge of AI ethics, bias detection, and responsible AI practices
· Ability to build AI prototypes and integrate with existing codebases
· A very high attention to detail and ability to thoroughly think through complex AI system designs
Internship Duration: 3 months
Work Arrangement: Work from Office (WFO)
Location: New Jersey, USA
Note: No Visa Sponsorship is available for this position. Applicants are required to read, write, and speak the English language. Candidates within the Scottsdale area are preferred, but we are flexible on location. Interns are hired as temporary employees for a planned duration specified in the final offer letter. The company makes no express or implied commitment that your temporary employment will have a minimum or fixed term, as employment with CustomerInsights.AI is at\-will.
Pay: $1,000\.00 \- $4,000\.00 per month
Work Location: In person
Salary Context
This $12K-$48K 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 CustomerInsights.AI, 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. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($30K) sits 86% below the category median. Disclosed range: $12K to $48K.
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.
CustomerInsights.AI AI Hiring
CustomerInsights.AI has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Basking Ridge, NJ, US. Compensation range: $48K - $48K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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