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About This Role
Cinchio Solutions is looking for a talented Senior AI Technical Lead to join our team!
All applications should be submitted within 7\-days following posting of the job; applications will be prioritized based upon time submitted and may not be reviewed beyond this time. The employer reserves the right to shorten or expand this timeframe as necessary to the job scope.
Senior AI Technical Lead
Cinchio Solutions is a part of the SSA Group, a visitor services company that provides admissions, retail, food \& beverage, and catering to over 80 zoos, museums, aquariums, and iconic attractions in 26 states nationwide. Check out SSA at the below websites:
- www.thessagroup.com
- https://www.facebook.com/ssagrp/
- https://www.linkedin.com/company/thessagroup
- https://www.instagram.com/thessagroup/
Position Title: Senior AI Technical Lead
Reports to: CTO
Job Description:
The SSA Group has always had a deep\-rooted passion for technological innovation. Innovation that helps the SSA Group continuously reshape to align with changing consumer expectations. Innovation that drives a great guest experience and exceptional revenue results. That passion has cultivated a culture of excitement around technology, digital experiences, and a willingness to be continual learners and early adopters to bring the latest advancements to our guests.
As we prepare for the next 50 years, SSA is expanding its digital innovation team at Cinchio Solutions and making a significant investment in Artificial Intelligence technologies, intelligent automation, and data\-driven guest experiences. Cinchio Solutions are looking for a passionate and experienced AI technology leader to help define and build the next generation of intelligent products and services across hospitality, food \& beverage, retail, and guest engagement platforms.
The position of Senior AI Technical Lead will play a foundational role in establishing Cinchio’s AI engineering capability and delivering innovative customer\-facing solutions that create measurable operational and revenue impact across some of the most recognized cultural attractions in the country.
This role will combine hands\-on technical delivery with architectural leadership, mentoring, and strategic innovation.
Examples of initiatives this role may help deliver include:
- AI\-powered upsell and recommendation engines within ordering platforms
- Intelligent inventory and ingredient forecasting driven by local event, weather, and traffic data
- Personalized product recommendations based on guest ordering behavior and purchasing trends
- “Surprise \& Delight” loyalty features that dynamically reward guests using statistical and behavioral models
- AI\-driven operational insights for food \& beverage, retail, and admissions teams
- Intelligent automation and decision\-support tooling for venue operators
This role will tremendously impact some of the coolest cultural attractions on the planet and help support our partners’ mission of sustainability, conservation, and animal welfare education.
Responsibilities
- Lead the technical design and delivery of AI\-powered digital products and intelligent platform capabilities
- Help establish and evolve Cinchio’s AI engineering strategy, standards, tooling, and best practices
- Design scalable cloud\-native AI solutions using AWS, Kubernetes, and distributed systems architectures
- Collaborate with product, engineering, and operational stakeholders to identify high\-impact AI opportunities
- Develop proof\-of\-concepts and production\-grade AI solutions from ideation through deployment
- Build and integrate intelligent services, APIs, recommendation systems, and data\-driven automation capabilities
- Analyze large datasets to identify trends, behavioral patterns, and operational optimization opportunities
- Architect secure, scalable, and highly available AI\-enabled applications and services
- Mentor and support less experienced engineers and developers through technical leadership and collaboration
- Participate in architectural decision\-making, code reviews, and technical design discussions
- Work closely with engineering teams to ensure AI capabilities integrate cleanly into existing platforms and products
- Evaluate emerging AI technologies and identify opportunities for practical business application
- Ensure solutions comply with security, reliability, and operational best practices
- Troubleshoot, optimize, and continuously improve AI\-driven systems and workflows
- Support Agile software development methodologies and contribute to continuous improvement initiatives
Requirements:
- Bachelor’s Degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or similar relevant experience
- 7\+ years of software engineering and digital product development experience
- 3\+ years of experience designing or implementing AI, machine learning, predictive analytics, recommendation systems, intelligent automation solutions, or LLM\-powered applications
- Proven experience designing and deploying cloud\-native applications on AWS
- Strong experience with Kubernetes and containerized application architectures
- Strong experience developing backend services and APIs using Golang or similar modern programming languages
- Strong experience with Python development, particularly within AI, machine learning, data engineering, automation, or LLM\-driven application environments
- Experience working with relational databases such as PostgreSQL and distributed data architectures
- Experience building scalable, secure, and highly available systems in production environments
- Understanding of modern AI technologies, LLMs, data pipelines, vector search, recommendation systems, and intelligent workflow automation
- Experience integrating third\-party APIs, data services, and event\-driven systems
- Strong understanding of software architecture, SDLC methodologies, and engineering best practices
- Experience working within Agile development environments
- Proven ability to balance hands\-on development with architectural leadership responsibilities
- Strong communication and collaboration skills with both technical and non\-technical stakeholders
- Public Cloud certifications and AI\-related certifications preferred
- Experience mentoring or guiding less experienced engineers preferred
Core Competencies:
- Excellent interpersonal and communication skills, including exhibiting professionalism and diplomacy when working with teams and business stakeholders
- Proven experience coordinating multiple initiatives simultaneously while meeting deadlines in fast\-paced environments
- Efficiency: Able to produce significant output with minimal wasted effort
- Attention to detail: Does not let vital details slip through the cracks or derail a project
- Endurance: Demonstrates tenacity and willingness to go the distance to get something done
- Proactivity: Acts without being told what to do and brings innovative ideas to the organization
- Flexibility/Adaptability: Adjusts quickly to changing priorities and conditions
- Strategic thinking/visioning: Able to communicate long\-term technical vision while balancing practical delivery needs
- Creativity/innovation: Generates new and innovative approaches to technical and business challenges
- Teamwork: Reaches out to peers and collaborates effectively across engineering, product, and operational teams
- Mentorship: Demonstrates a willingness to support and develop less experienced colleagues through guidance and knowledge sharing
Location \& Travel:
- This position is remote\-based within the United States
- Infrequent travel to Denver may be required for planning sessions, workshops, and team collaboration
This is by no means an exhaustive list of all responsibilities, skills, duties, requirements, efforts or working conditions associated with this job description. The Company reserves the right to revise the job description or to require that other or different tasks are performed when circumstances change (i.e., emergencies, changes in personnel, workload, rush jobs or technological developments)
Compensation and Benefits
- Full\-Time, Exempt
- $75 monthly cell phone stipend
- Flex Time Off: no accruals; employees are encouraged to schedule time off as needed within business scope.
- Medical, Dental, Vision, Life Insurance and other voluntary benefits for you and your family; employee premiums applicable.
- Participation in a 401(k) program with a 15% company match (must be 21 years or older, eligible after one year of employment with 1,000 hours worked, available to enroll during Open Enrollment Periods).
- Short\-Term Disability and Long\-Term Disability, employer sponsored; scaled salary pay following submission and approval of leave
- Parental Leave: Birthing Parent Plan covers up to (6\-8\) weeks fully paid leave, based on the birthing event.
- SSA Paid Benefit: Up to 120 hours of Paid Leave for qualifying reasons, including Parental Bonding and your family’s serious medical conditions.
- Up to 5 days Paid Bereavement Leave
- On\-Demand Pay Program: Get access to a portion of earned wages before payday.
- Meal Plan \& Employee Discounts where applicable
- Paid sick leave is provided in accordance with applicable state and local laws. Accrual rates, caps, and usage rules vary by location.
Locations include: Arizona, California, Chicago, Pittsburgh, Connecticut, Illinois, Massachusetts, Michigan, Minnesota, Missouri, New Mexico, New York, Rhode Island, Washington, D.C.. Colorado: Employees accrue 1 hour of paid sick leave for every 30 hours worked, up to 48 hours per year, under the Healthy Families and Workplaces Act (HFWA). Maryland: Employees accrue at least 1 hour of paid sick and safe leave for every 30 hours worked, up to 40 hours per year, as required under the Maryland Healthy Working Families Act. Washington: Employees accrue 1 hour of paid sick leave for every 40 hours worked, in accordance with the Seattle Paid Sick and Safe Time.
SSA Holdings and its affiliated companies, including SSA Group, A\&F Souvenir, Cinchio Solutions, and Behavioral Essentials, are equal opportunity employers. We are committed to diversity and inclusion in our hiring practices and welcome applicants from all backgrounds. A diverse team strengthens our collective impact.
All California Residents: By submitting your job application, you agree you have reviewed the SSA Group California Consumer Privacy Act (CCPA) Candidate and Employee Privacy Notice ("Notice").
San Francisco Residents: Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Please see the "Fair Chance Ordinance \- Know Your Rights" document for more information. By submitting your job application, you agree you have reviewed the "Fair Chance Ordinance \- Know Your Rights" document.
Salary Context
This $175K-$190K range is above 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 The SSA Group, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $175K to $190K.
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.
The SSA Group AI Hiring
The SSA Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $190K - $190K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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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