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About This Role
locations
California, USA
United States
time type
Full time
posted on
Posted Today
job requisition id
JR111963Be 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.
The Genesys Partner AI Architect is a is a senior presales AI specialist who operates across four core motions:
1\. Driving strategic Genesys Cloud CX and Genesys AI opportunities with Genesys Strategic Technology Partners, Global Systems Integrators (GSI), and Genesys sales teams showcasing the Genesys differentiated Genesys AI experience.
2\. Scaling the Genesys partner ecosystem through creation of reusable assets, reference architectures, Pre\-Sales technical enablement for Genesys partner’s Pre\-Sales teams, and coaching. This role is pivotal in building assets that leverage the strengths of both Genesys and Genesys partners, allowing them to demonstrate the value of their solutions in combination with Genesys Cloud CX and Genesys AI capabilities.
3\. Ensuring that partners understand the Genesys AI business and technical value proposition and are able to independently generate pipeline, articulating value, and delivering AI led outcomes. This is anchored in structured Pre\-Sales enablement, repeatable frameworks, and targeted engagement models.
4\. Shaping product and go\-to\-market direction through real\-world partner and customer feedback
This role requires a blend of deep AI technical acumen, exceptional sales \& communication skills, and the ability to translate complex capabilities into clear, actionable business value.
Key Responsibilities:
- Design end\-to\-end AI systems for customer experience use cases. Architect reliable, production\-ready AI solutions that go beyond prompt design, combining LLMs, deterministic workflows, tools, and orchestration layers. Define how AI interacts across the full journey (self\-service, agent copilot, journey management, and back\-office automation), including fallback strategies, human handoff, and failure handling.
- Optimize retrieval\-augmented generation (RAG) and knowledge architectures to extend Genesys solutions where required. Design scalable knowledge and retrieval strategies that ground AI responses in enterprise data. Develop approaches for content structuring, chunking, embedding, and ranking to ensure accuracy, relevance, and freshness. Partner with customers to align AI outputs with trusted knowledge sources while balancing performance, latency, and governance requirements.
- Establish AI evaluation frameworks and quality measurement strategies \- Define how success is measured for AI\-driven experiences, including accuracy, containment, customer satisfaction, and business impact. Create test sets, evaluation methodologies, and feedback loops to continuously improve performance. Translate technical quality metrics into business\-relevant outcomes to support customer decision\-making and adoption.
- Engineer contextual AI experiences that leverage real\-time data and conversation state \- Design how AI systems incorporate dynamic context such as customer data, interaction history, and external signals. Optimize context management and prompt structure to maximize relevance while managing token limits and response quality. Ensure AI interactions remain coherent, personalized, and aligned across channels and touchpoints.
- Design for scalability, latency, and cost efficiency in enterprise environments \- Evaluate and optimize AI solutions for real\-world constraints, including response time (especially for voice), throughput, and cost at scale. Make informed tradeoffs across model selection, caching strategies, and architecture patterns to deliver performant and economically viable solutions. Ensure designs meet enterprise expectations for reliability and responsiveness.
- Support Partner Development Lab Co\-created Configurations: Work with Genesys Strategic Technology partners and GSI partners to build, refine, and optimize their Genesys Cloud development lab environments with a focus on AI. These labs will serve as the foundation for joint innovation, ensuring that both Genesys and partner teams can continuously iterate on solutions and use cases. This includes leading and facilitating remote and in person workshops educating and empowering Partners on the Genesys unique value proposition, roadmaps to help generate pipeline, help them to be more self\-sufficient and drive Genesys Cloud and AI adoption.
- Technical Escalation: Serve as a technical escalation point for difficult or complex integration issues that arise during partner Pre\-Sales cycles. Provide advanced troubleshooting and solution development to resolve roadblocks effectively.
- Mentor Genesys Partner’s Solution Consultant teams and act as a technical escalation point for complex technical issues.
- Technical Field Readiness Assistance: Work with GSI and Genesys Strategic Technology partners, Genesys Product Management, Genesys Product Marketing, and Genesys Enablement teams to assist with technical field readiness. This may involve building assets, shared demo environments, videos, and assisting with RFx’s.
- Support Strategic Pre\-Sales Engagements: Co\-sell with partners and Genesys Account Teams (when requested) in high impact opportunities by leading technical discovery, solution design, demonstrations, workshops, and value articulation.
- Establish best practice frameworks: Create and maintain structured knowledge\-sharing model across partners and internal teams including demos, success stories, and reusable assets.
- Leverage expert\-level knowledge of Genesys AI capabilities — including predictive AI and Agentic Virtual Agents —to articulate and demonstrate product value to customers and prospects.
- Design and deploy AI prototypes in sandbox and/or partner and customer development environments to validate use cases, integrations, latency, and success criteria, and to highlight the differentiated value of Genesys AI.
- Support partners, Genesys account teams, and professional services to transition successful prototypes into production pilots supporting technical handoff, hardening, KPI alignment, and business outcome validation through hands\-on configuration and building of the production pilots.
- Build integrations to third\-party systems via RESTful APIs and emerging interoperability patterns such as MCP and A2A showcasing the art of the possible with Genesys Cloud AI solutions.
- Develop reusable technical assets and enable Solution Consultants, partners, and account teams through workshops, deep\-dive demonstrations, coaching, and scalable technical content.
- Provide technical feedback and strategic insights to Product Management and Engineering on AI product design, implementation considerations, and customer\-driven enhancements.
- Support partners in influencing CIO/CTO\-level stakeholders and position Genesys as integral to an organization’s broader IT and transformation strategy.
Requirements:
- AI Architecture \& Technical Judgment: Hands\-on experience designing modern AI solutions for customer experience orchestration and contact center use cases using the right mix of classic ML, NLU/NLP, retrieval\-based systems, LLMs, orchestration patterns, prompt engineering best practices, tool use and agentic approaches. Ability to make and defend architecture tradeoffs across latency, cost, explainability, governance, multilingual requirements, and business risk.
- Enterprise Integration \& Real\-Time Design: Expertise integrating AI solutions with enterprise platforms, knowledge sources, RESTful APIs, event\-driven architectures, identity systems, and broader cloud ecosystems. Able to design for real\-world constraints including voice latency, fallback paths, throughput, reliability, and secure data access.
- Evaluation, Governance \& Observability: Ability to define AI evaluation strategies, success metrics, and monitoring approaches across offline and online testing, retrieval quality, tool\-call accuracy, safety, drift, auditability, and cost control.
- Executive Communication \& Hands\-On Partner Pre\-Sales Enablement: Strong ability to translate complex AI architectures into clear business value for technical and executive stakeholders, while also creating reusable assets, workshops, and enablement content that scale field capability across partner organizations.
- Prompt Design \& AI Workflow Optimization: Practical experience designing prompts, context strategies, and orchestration flows as part of a broader system architecture, rather than as a standalone discipline.
- Technical Demonstration Prowess: Proven ability to create, deliver, and adapt compelling technical demonstrations and presentations that clearly articulate AI integration points and business impact.
- Sales Acumen: Demonstrated success partnering with sales teams to understand customer challenges and provide AI\-focused technical solutioning.
- Business Value Orientation: Ability to identify opportunities for process optimization and recommend AI\-driven solutions that enhance customer outcomes and operational efficiency.
- Executive Presence: Proven ability to influence CIO/CTO decision\-makers.
- Strong understanding of the Genesys Cloud platform and Genesys AI preferred.
- Hands on experience working with AWS, Salesforce, ServiceNow, Meta, and/or Guidewire.
- Prior experience working with GSI’s is preferred.
\#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.
$111,700\.00 \- $196,300\.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.
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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. With agentic AI at its core, Genesys Cloud™ is the AI\-Powered Experience Orchestration platform that connects people, systems, data and AI across the enterprise. 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.com.
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 $111K-$196K range is in the lower quartile for AI Architect roles in our dataset (median: $181K across 29 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,317 AI roles we're tracking, AI Architect positions make up 1% of the market. At Genesys, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $237,300 based on 102 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($154K) sits 35% below the category median. Disclosed range: $111K to $196K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Genesys AI Hiring
Genesys has 2 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span CA, US, MA, US. Compensation range: $196K - $238K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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