Interested in this AI/ML Engineer role at COCC?
Apply Now →About This Role
Who we are...
As an industry\-leading fintech provider, COCC delivers innovative, comprehensive technology solutions and strategic partnerships throughout the Northeastern United States. Listed among American Banker's FinTech 100 and the Inc. 5,000 fastest growing companies in the nation, COCC inspires the industry with innovation and top\-quality support. Designated a Top Workplace in Connecticut and a nationally Certified Great Place to Work, COCC recognizes employees as the core of our success.
Inspiring you to become extraordinary in work and life.
What we need…
COCC is seeking an experienced AI Governance Program Manager to lead and mature our enterprise AI governance, risk, and compliance program. This role is ideal for a seasoned GRC professional who understands AI and emerging technologies and can bring structure, oversight, and clarity to how AI is deployed across a regulated environment. You will play a critical role in ensuring AI capabilities are used responsibly, securely, and in alignment with regulatory expectations and organizational risk appetite. You will work closely with security, legal, technology, and business teams to shape and evolve our enterprise AI governance program.
What’s in it for you…
COCC offers a collaborative environment, career growth, and all the benefits you’d expect from an award\-winning employer, including:
- Hybrid schedules and ample paid time off allowing you work/life balance and flexibility
- Customized training and onboarding to support you in your first year at COCC
- Robust employee development programs aligned with career pathing objectives
- Cutting\-edge training and educational resources from vendors like SANS, PluralSight and CBTNuggets
- Generous PTO offerings, benefits and competitive compensation
- On\-site fitness centers, wellness incentives, and lifestyle spending accounts
- Tuition Reimbursement
- One\-on\-one career coaching
- DEIB initiatives championing inclusion and encouraging you to bring your whole self to work
- Financial planning assistance with certified professionals
- Peer recognition programs
What you’ll do…
- Develop, implement, and maintain the enterprise AI governance framework, policies, standards, and procedures
- Establish AI risk management processes aligned with regulatory expectations and industry frameworks
- Define AI governance roles, responsibilities, accountability structures, and escalation processes
- Establish AI risk tiering and classification methodologies
- Conduct and oversee AI risk assessments for internally developed AI, third party platforms, vendors, and customer facing AI solutions
- Evaluate AI risks related to security, data exposure, bias, explainability, model drift, and regulatory compliance
- Partner with security and architecture teams to define compensating controls and risk treatment plans
- Maintain the enterprise AI inventory and AI risk register
- Monitor evolving AI regulations, supervisory guidance, and industry expectations
- Support internal audits, regulatory examinations, and external assessments related to AI governance
- Develop governance reporting, metrics, and artifacts for regulators and executive leadership
- Coordinate AI related policy exceptions, approvals, and risk acceptances
- Partner with vendor management and procurement teams to assess AI risks in third party relationships
- Collaborate with information security teams to support secure AI deployment and data protection practices
- Prepare and present AI governance and risk posture updates to senior leadership and board committees
- Drive enterprise awareness and training initiatives related to responsible AI use
What You’ll bring...
- Masters degree in Cybersecurity, Information Technology, Risk Management, Data Science, or a related field preferred
- Seven or more years of experience in information security, technology risk, IT audit, or GRC
- Experience working in regulated industries such as financial services, fintech, healthcare, insurance, or critical infrastructure
- Strong understanding of AI and machine learning technologies including generative AI and large language models
- Experience developing governance frameworks, policies, standards, and risk assessment methodologies
- Familiarity with NIST AI RMF, NIST Cybersecurity Framework, FFIEC guidance, GLBA, SOC examinations, and privacy regulations
- Strong analytical, communication, and program management skills
- Ability to translate technical AI concepts into business risk language
Salary range for this role is $150K\-$195K per year
Applicants for employment in the US must have work authorization that does not currently or in the future require sponsorship of a visa for employment authorization in the United States.
COCC is committed to maintaining a drug\-free workplace. All applicants are required to pass a credit, background, and substance test prior to employment. COCC procures background and consumer reports in compliance with all Federal and State regulations, including The Fair Credit Reporting Act and applicable Department of Labor laws regarding pre\-employment screens. COCC is an equal opportunity employer committed to a community of inclusion, and an environment free from discrimination, harassment, and retaliation.
Accessibility \- If you’re a job seeker with a disability and require accessibility assistance or an accommodation to apply for one of our jobs, please let us know by calling 860\-678\-0444 or emailing [email protected]. Please specify the help you need and we’ll be happy to get back to you.
Salary Context
This $150K-$195K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At COCC, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($172K) sits 5% below the category median. Disclosed range: $150K to $195K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
COCC AI Hiring
COCC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rocky Hill, CT, US. Compensation range: $195K - $195K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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