Interested in this AI/ML Engineer role at Finger Lakes Community College?
Apply Now →About This Role
The AI \& OER Librarian is responsible for the assessment and implementation of artificial intelligence technologies within library operations, educational programming, and scholarly activities. This position emphasizes strengthening student competencies in information literacy, with particular attention to promoting responsible AI practices, while creating materials that enable faculty and students to effectively traverse the changing technological environment. The librarian will also serve as a key resource for embedding information literacy and AI skills throughout academic programs to advance the SUNY General Education Information Literacy Core Competency objectives.
Additionally, working with the Director of Library Learning Resources, the AI \& OER Librarian is responsible for the planning, coordination, and implementation of the Zero Cost Degree program as well as other OER and affordable course content initiatives at Finger Lakes Community College. The librarian will provide professional development opportunities for faculty and staff; oversee faculty contracts for OER/AIM adoption compensation; work with campus constituents to implement a process that enables faculty to identify zero/affordable course material courses at the time of course development/identification each semester so that Students will be able to search for zero cost or low\-cost courses when searching for classes each semester; develop documentation to be disseminated on the College's webpage as well as directly to students about zero/low\-cost course offerings.
FLCC is committed to diversity, equity and inclusion, and strives to provide an environment that embodies these principles. We strive to attract talented personnel from diverse backgrounds and traditions, particularly of race and ethnicity, gender, gender identity, sexual orientation, nationality, culture, religion, worldview and physical and mental abilities. As such, applicants from historically under\-represented groups are highly encouraged to apply.
Qualifications:
Educational Background: ALA\-accredited MLS (Master of Library Science) or MLIS (Master of Library \& Information Science) degree required.
Required Experience:
Strong understanding of artificial intelligence (AI) applications in education, research, and digital literacy; familiarity with ethical considerations in AI, including bias, misinformation, and academic integrity; understanding of copyright, open licensing, fair use, author rights, archiving rights, and institutional repositories. The successful candidate must have a strong understanding of open educational resources (OER) and open pedagogy, strong customer service ethic focus with a passion for the profession. Successful candidate must have the ability to build collaborative relationships with students, faculty, staff, and other College stakeholders, the ability to work occasional nights and weekends, and a commitment to diversity, equity, and inclusion in an educational setting. Recent graduates are encouraged to apply.
Preferred Experience:
Experience working in an academic library, preferably in a community college setting. Experience in reference, library instruction, and collection development. Possesses current knowledge of trends in library services and engagement; knowledge of AI tools such as ChatGPT and other generative AI platforms; experience with learning management systems (e.g., D2L) and instructional technologies.
Specific Responsibilities:
AI:
- Creates and facilitates educational programs in information literacy with a specialized focus on AI competency and responsible AI implementation.
- Partners with academic faculty to embed AI technologies and information literacy competencies within course content, supporting SUNY General Education requirements for AI and Information Literacy.
- Offers scholarly support and consultation on artificial intelligence applications, information literacy practices, and evolving digital technologies.
- Coordinates educational workshops and professional development opportunities covering AI utilization in academic scholarship, assessment of AI\-produced materials, and conscientious AI practices.
- Maintains expertise in current AI developments and digital literacy trends.
- Advocates for responsible AI practices and scholarly integrity through community engagement, educational initiatives, and institutional collaborations.
- Works alongside IT services and other constituents to implement AI\-enhanced solutions within library operations.
- Serves as the Library's representative in institution\-wide AI policy and planning discussions.
???????OER:
- Works closely with Library Director to provide leadership, vision and coordination of the College's OER program.
- Develops and implements the Strategic Plan Grant Goals to develop an OER Degree Program as approved by the Board of Trustees:
+ Works with campus constituents to identify an appropriate degree program and collaborate with faculty and departments for AIM/OER conversion so that FLCC will be the first SUNY College to offer an associate degree program with zero/low materials cost.
+ Works with librarians, faculty, as well as IT, advising and registration departments, to develop and implement that enables faculty to identify zero/affordable course material courses at the time of course development/identification each semester so that students will be able to search for zero cost or low cost courses when searching for classes each semester.
+ Develops documentation to be disseminated on the College's webpage as well as directly to students about zero/low cost course offerings so that students will be have a clear roadmap of which courses are available at zero/low cost.
+ Works with SUNY OER to develop professional development opportunities that inform the campus community of the benefit of zero/cost course adoption and assist faculty with the conversion process.
- Assists faculty with open content adoption, production, and publishing and provides guidance throughout the OER/ affordable content conversion process.
- Manages OER grants and budgets.
- Supervises OER fellows/staff.
- Acts as library liaison to SUNY OER services and campus constituents.
???????Other:
- Functions as part of a library team that works with faculty, library staff, and library patrons to ensure library services, programs, and other related issues meet academic and community needs.
- Participates in the work of the college community.
- Keeps abreast of current library trends and emerging technologies through professional activities.
- Assists staff with cataloging issues.
- Participates in the marketing, development, implementation, assessment and provision of programs and services to ensure greater awareness of the Library and its services to library patrons and stakeholders.
- Supervises clerical or work study students when needed.
- Provides occasional assistance at the circulation desk.
- Teaches information literacy/library instruction classes.
- As needed, represents the library on various college committees.
- Develops research guides and online tools that assist faculty and students in using the libraries and information sources.
- Assumes other responsibilities at the request of the library director to ensure quality library services.
Benefits and salary range:
Applicants receiving benefits through the New York State Retirement System should review state regulations regarding post\-retirement employment. This position is not eligible for employment under a Section 211 waiver.
- 2026/2027 starting salary range of $60,103 to $63,108 and is commensurate with qualifications
- A flexible work schedule is available upon successful completion of training. This position follows a hybrid model, with a combination of on\-site and remote work.
- NYS retirement or Optional SUNY Retirement Program
- SUNY Voluntary Savings Plan
- Comprehensive Health and Dental Insurance through Excellus BCBS
- FSA
- 21 days of annual leave, 12 sick days, and 13\.5 holidays annually
- FLCC Tuition Waiver for employee and dependents
- Tuition assistance – 4\-year institutions
- This is a full\-time grant funded position
Special Notes: Visa sponsorship is not available for this position. If you currently need sponsorship or will need it in the future to maintain employment authorization, you do not meet eligibility requirements.
Application Instructions:
Applicants interested in applying MUST submit the following documents via online:
- Resume/Cv
- Cover letter
Applications will only be accepted online. Preferred consideration will be given to applicants who apply by August 2nd.
*Finger Lakes Community College does not discriminate against any employee, applicant for employment, student or applicant for admission based on an individual’s race, color, national origin, religion, creed, age, disability, sex, gender identification, gender expression, sexual orientation, familial status, pregnancy, predisposing genetic characteristics, military status, veteran status, domestic violence victim status, criminal conviction or any other category protected by law. The College adheres to all federal and state civil rights laws prohibiting discrimination in public institutions of higher education.*
Salary Context
This $60K-$63K range is in the lower quartile 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 Finger Lakes Community College, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($61K) sits 72% below the category median. Disclosed range: $60K to $63K.
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.
Finger Lakes Community College AI Hiring
Finger Lakes Community College has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Canandaigua, NY, US. Compensation range: $63K - $63K.
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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