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About This Role
locations North Carolina, USA Indiana, USA Texas, USA New York, USA South Carolina, USA View All 12 Locations
time type Full time posted on Posted 2 Days Ago job requisition id JR112387Be the one building AI-powered experiences where they matter most.
At Genesys, we help organizations create better customer experiences through AI-powered experience orchestration. Our platform connects people, systems, data and AI to help organizations deliver more personalized service, improve operational efficiency and build stronger customer relationships.
Help build, support and operate technology used by more than 8,000 organizations in over 100 countries – moving AI from possibility to production in real-world enterprise environments every day.
Be the one growing faster in the AI era.
At Genesys, we're building a high-performance workplace designed for people who want to grow quickly, do meaningful work and build lasting careers in AI.
Location
North America
Mission:
Design and lead the implementation of AI-powered digital customer experience solutions that achieve business outcomes while ensuring technical excellence, scalability, security, and operational readiness.
Our Professional Services organization is where AI strategy meets delivery. We are a globally distributed team of consultants and architects united by a single purpose: helping enterprise customers realize the transformative potential of AI-powered digital customer experience — not in theory, but in production.
We operate across regions and time zones, bringing together deep technical expertise, design thinking, and industry knowledge. Our work spans hands-on project delivery, rapid experimentation, applied AI, and the development of reusable assets and reference architectures that raise the bar for the entire practice.
We believe the best solutions are built by people who are simultaneously curious and rigorous — willing to explore emerging technologies while remaining accountable for successful customer outcomes.
We are now looking for a Lead AI Solutions Architect based in the North America to join us as a senior technical voice in complex customer programs. If you thrive at the intersection of AI innovation, digital customer engagement, and enterprise architecture, and you want your work to have a direct impact on customer experiences, read on.
Key Responsibilities:
Solution Architecture
- Define end-to-end AI-powered digital solution architectures for customer programs.
- Design solutions across Genesys Cloud digital channels, including Web Messaging, SMS, Email, digital routing, and related capabilities.
- Design integrations between Genesys Cloud, AI services, enterprise applications, data platforms, and third-party technologies.
- Establish architectural standards for scalability, resilience, security, and maintainability.
- Review and provide technical guidance on solution designs produced by project teams.
Technical Leadership
- Serve as the technical authority for assigned customer engagements.
- Guide consultants and implementation teams through complex technical challenges.
- Lead architecture workshops, discovery sessions, and technical design discussions with customers.
- Provide governance throughout delivery to ensure alignment with approved architecture, project scope, and customer requirements.
AI & Digital Design
- Design AI use cases including digital virtual agents, digital bot flows, agent copilots, knowledge systems, workflow automation, and other AI-enabled digital experiences.
- Design customer journeys that integrate AI capabilities with Genesys Cloud digital channels and routing.
- Define data requirements, integration patterns, and AI governance considerations.
- Ensure responsible AI practices are incorporated into solution designs.
- Evaluate emerging Genesys Cloud Digital and AI capabilities and determine how they can be applied to customer use cases.
Customer Engagement
- Advise customer technical stakeholders on architecture decisions and trade-offs.
- Translate customer business requirements and digital use cases into practical technical solutions.
- Present technical recommendations to enterprise architects, IT leaders, platform owners, and business stakeholders.
- Build trusted-advisor relationships with customer technical teams.
- Identify technical risks, dependencies, and scope concerns early and communicate them clearly to project leadership.
Practice Contribution
- Contribute reusable assets, reference architectures, implementation patterns, and best practices.
- Mentor consultants and senior consultants.
- Support presales activities for complex Digital and AI opportunities.
- Contribute to internal enablement and knowledge sharing as Genesys Cloud Digital and AI capabilities continue to evolve.
Typical Scope
- Large enterprise customer implementations
- Complex Digital and AI programs
- Single strategic programs or multiple concurrent customer engagements
- Technical lead for major Digital deployments
- Influences project and account-level technical decisions
Qualifications:
- 7+ years of experience in solution architecture, technical consulting, enterprise software delivery, or customer experience technology, with significant experience in Digital, AI, conversational AI, or CX platforms.
- Hands-on familiarity with Genesys Cloud or comparable CCaaS or CPaaS platforms, particularly digital channels, routing, Architect, and developer APIs.
- Demonstrated experience designing and implementing digital customer experiences across technologies such as Web Messaging, SMS, Email, digital routing, or comparable messaging channels.
- Proven experience designing and deploying AI-powered solutions such as digital virtual agents, conversational AI, LLM-based copilots, knowledge management systems, digital bot experiences, or workflow automation.
- Strong understanding of enterprise integration technologies including REST APIs, JSON, authentication and identity protocols, SDKs, and cloud-based integration patterns.
- Solid understanding of cloud architecture patterns across AWS, Azure, or GCP.
- Experience leading architecture and technical design sessions, producing technical documentation, and presenting recommendations to senior technical and non-technical stakeholders.
- Ability to independently troubleshoot complex technical challenges and work across Product, Engineering, Care, project teams, and customer technical organizations to drive resolution.
- Strong communication and customer-facing skills with the ability to translate complex technical concepts for business audiences and business requirements into technical designs.
- Ability to operate effectively in a global, matrixed Professional Services environment while managing scope, quality, customer relationships, and multiple priorities.
- Familiarity with responsible AI principles, data governance, and security-by-design practices in enterprise environments.
- Genesys Cloud certifications and experience with capabilities such as Work Automation, Knowledge Fabric, Agent Copilot, Virtual Agents, GPE, GPR, Salesforce, ServiceNow, Microsoft Dynamics, or SharePoint are a plus.
#LI-CP1 #LI-Remote
Compensation:
This role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidate’s experience, qualifications, skills, and location. This role might also be eligible for a commission or performance-based bonus opportunities.
$110,600.00 - $194,400.00Benefits:
- Medical, Dental, and Vision Insurance.
- Telehealth coverage
- Flexible work schedules and work from home opportunities
- Development and career growth opportunities
- Open Time Off in addition to 10 paid holidays
- 401(k) matching program
- Adoption Assistance
- Fertility treatments
Click here to view a summary overview of our Benefits.
Working at Genesys
- AI at enterprise scale – Build, support and operate AI-powered technology used by more than 8,000 organizations worldwide. 150+ new AI features were released in the last fiscal year.
- A flexible-first culture – Join a global team of nearly 7,000 employees with flexible ways of working designed to help people do their best work.
- Growth in the AI era – Build future-ready skills through mentorship, learning programs, leadership development and education support.
- Time to recharge and give back – Benefits include paid volunteer time, August Free Fridays, well-being resources and regionally tailored programs for employees and their families.
- Recognized globally – Genesys is Great Place to Work® certified in 17 countries and 94% of employees are proud to tell others they work at Genesys.
Learn more about our culture, AI innovation and sustainability commitments through our Careers site and Sustainability Report.
What Happens After You Apply
After you apply, here's what you can typically expect:
- Our Talent Acquisition team reviews your application with the hiring team.
- A Talent Acquisition Partner will review your application and, if your background is aligned, schedule a Zoom interview.
- Next, you'll meet the hiring manager and other members of the interview team.
- We aim to keep the process focused and respectful of your time, with no more than five interviews in most cases.
- After interviews are complete, our team will follow up with the final steps.
Every application is reviewed by a person. Response times may vary by role and location, but our team will keep you informed throughout the process.
Stay Connected
Stay connected to learn more about how we're applying AI to customer and employee experience challenges and get notified when relevant opportunities become available.
Get notified about relevant opportunities.
Be the one building what's next - where AI, experience and impact come together.
Employee Referral
If a Genesys employee referred you, please apply using the link they shared so we can connect your application to their referral.
About Genesys:
Genesys® empowers more than 8,000 organizations worldwide to create the best customer and employee experiences. Genesys Cloud™ is the Agentic Orchestration Platform that securely connects people, systems, data and AI across the enterprise with built-in governance and control. As a result, organizations can drive customer loyalty, growth and retention while increasing operational efficiency and teamwork across human and AI workforces. To learn more, visit www.genesys.ai.
Reasonable Accommodations:
If you require a reasonable accommodation to complete any part of the application process, or are limited in your ability to access or use this online application and need an alternative method for applying, you or someone you know may contact us at [email protected].
You can expect a response within 24–48 hours. To help us provide the best support, click the email link above to open a pre-filled message and complete the requested information before sending. If you have any questions, please include them in your email.
This email is intended to support job seekers requesting accommodations. Messages unrelated to accommodation—such as application follow-ups or resume submissions—may not receive a response.
Genesys is an equal opportunity employer committed to fairness in the workplace. We evaluate qualified applicants without regard to race, color, age, religion, sex, sexual orientation, gender identity or expression, marital status, domestic partner status, national origin, genetics, disability, military and veteran status, and other protected characteristics.
Please note that recruiters will never ask for sensitive personal or financial information during the application phase.
Salary Context
This $110K-$194K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Genesys, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($152K) sits 28% below the category median. Disclosed range: $110K to $194K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Genesys AI Hiring
Genesys has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span NC, US, TX, US, Massachusetts, United States. Compensation range: $194K - $236K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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