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About This Role
locations Virtual Office (Texas) Florida, USA Georgia, USA North Carolina, USA time type Full time posted on Posted Today job requisition id JR110789Be 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.
Role Overview:
This role shapes how enterprises adopt and scale AI to transform customer experience outcomes across Genesys Cloud. You will operate as a strategic and technical leader, translating complex business challenges into AI-driven solutions that improve customer engagement, operational efficiency, and measurable performance. At Genesys, we are redefining customer experience through empathy and AI innovation, helping organizations deliver more human, connected interactions at global scale.
You will own the end-to-end lifecycle of Agentic AI orchestration, from discovery through continuous optimization, directly influencing enterprise AI strategy and execution. Partnering with senior stakeholders, you will align AI initiatives to business KPIs, ensuring every solution delivers tangible impact such as improved containment, reduced cost to serve, or increased customer satisfaction. Your work will help organizations move beyond experimentation into sustained, enterprise-wide transformation.
This role offers exposure to advanced AI capabilities, including virtual agents, copilots, and orchestration frameworks, within complex, multi-system environments. You will collaborate across product, engineering, and customer teams to accelerate innovation while maintaining governance, scalability, and long-term value realization.
Key Responsibilities:
- Lead end-to-end delivery of Agentic AI solutions that improve customer experience metrics and operational performance
- Translate business objectives into scalable AI architectures that drive automation, efficiency, and measurable ROI
- Design integration patterns and data flows that enable seamless orchestration across channels, platforms, and enterprise systems
- Drive alignment across customer stakeholders and internal teams to accelerate AI adoption and ensure successful outcomes
- Deliver rapid POCs and MVPs using Genesys Cloud AI capabilities, enabling faster time to value and scalable production deployment
- Optimize AI solutions post-launch through continuous iteration, improving accuracy, performance, and business impact
- Influence customer AI strategies by advising on architecture, platform capabilities, and long-term transformation roadmaps
- Champion responsible AI practices, ensuring compliance with security, privacy, and regulatory standards such as HIPAA and PCI
Required Qualifications:
- 8+ years of experience delivering CX, CRM, or AI-driven solutions in enterprise environments
- Strong expertise in cloud platforms such as AWS, Azure, or GCP, including experience with distributed architectures
- Proven experience with APIs, data pipelines, and modern integration patterns such as REST, JSON, and event-driven systems
- Demonstrated ability to translate business requirements into scalable technical solutions with measurable outcomes
- Experience working with conversational AI, NLP, or AI-powered customer experience platforms
- Strong stakeholder engagement skills, including experience working with executive-level audiences
- Ability to operate independently in fast-paced, ambiguous environments
- Solid understanding of data governance, security, and compliance frameworks
Preferred Qualifications:
- Experience with Genesys Cloud or other contact center platforms
- Familiarity with LLM integrations, agent-assist technologies, or AI orchestration frameworks
- Experience with customer journey mapping and omnichannel experience design
- Background in industries such as Financial Services, Healthcare, Insurance, Retail, or Public Sector
- Experience supporting large-scale digital transformation or enterprise AI adoption initiatives
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.
$134,300.00 - $236,100.00Benefits:
- 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 $134K-$236K range is above 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 ($185K) sits 13% below the category median. Disclosed range: $134K to $236K.
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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