AI Engineer

• Design AI solutions for customer experience use cases, including self-service, agent assistance, journey management, and back-office automation.
• Support solution designs that combine LLMs, deterministic workflows, tools, orchestration layers, fallback strategies, and human handoff patterns.
• Support retrieval-augmented generation, knowledge, and content strategies that help ground AI responses in trusted enterprise data.
• Assist with approaches for content structure, chunking, retrieval, relevance, and freshness while balancing performance, latency, and governance considerations.
• Help define AI evaluation approaches and quality measurement strategies for customer-facing AI experiences.
• Support the creation of test sets, feedback loops, and success metrics tied to accuracy, containment, customer satisfaction, business impact, and adoption.
• Assist in designing contextual AI experiences that use real-time data, conversation state, customer information, interaction history, and external signals to create more relevant and personalized customer and employee experiences.
• Support solution design decisions related to scalability, latency, reliability, and cost efficiency in enterprise environments.
• Help evaluate trade-offs across model selection, caching, orchestration, architecture patterns, and production-readiness considerations.
• Leverage strong working knowledge of Genesys AI capabilities to articulate and demonstrate product value to customers and prospects.
• Support presales activities including technical discovery, solution design, product demonstrations, sandbox and trial engagements, AI integration guidance, and value assessments.
• Design and deploy AI prototypes in sandbox and/or customer development environments to validate use cases, integrations, latency, success criteria, and differentiated Genesys AI value.
• Partner with account teams and Professional Services to help transition successful prototypes into production pilots, including technical handoff, solution hardening, KPI alignment, and business outcome validation.
• Build and support integrations to third-party systems via RESTful APIs and emerging interoperability patterns, showcasing what is possible with Genesys Cloud AI solutions.
• Develop reusable technical assets and help enable Solution Consultants, partners, and account teams through workshops, coaching, and scalable technical content.
• Provide field feedback and customer insights to Product Management and Engineering related to AI product design, implementation considerations, and customer-driven enhancements.
• Engage with technical and business stakeholders to position Genesys AI as part of a customer’s broader customer experience, IT, and transformation strategy.

AI Architect

• Lead the development of advanced AI and machine learning systems
• Design end-to-end AI solutions, optimize model performance
• Mentor junior engineers and support cross-team collaboration
• Document architectural decisions and best practices

Senior AI Engineer

• Manage client accounts and secure the delivery of exceptional results
• Foster sturdy relationships with clients to understand their needs
• Analyze complex data sets to derive actionable insights
• Confirm adherence to quality standards and established practices in deliverables

AI Engineer Manager

• Define and drive the vision, strategy, and roadmap for enterprise semantic data and data connectivity across business domains
• Partner with business and IT leaders to identify high-value opportunities to connect and standardize data across systems
• Position the semantic data product as a product, not just a technical asset, with clear customers, outcomes, adoption goals, and success metrics
• Define and evolve the product’s value proposition, ensuring alignment to business priorities and measurable outcomes
• Act as the general manager of the product, balancing user needs, technical feasibility, strategic priorities, operational constraints, and business impact
• Define and manage data products aligned to key business domains, including clear ownership, users, and success metrics
• Define target users and use cases for data products, ensuring alignment to specific decisions, workflows, and business outcomes
• Partner closely with business customers to ensure data products support critical decisions, workflows, and operational outcomes
• Drive adoption and usage of shared data assets across business units
• Identify and prioritize use cases where connected data unlocks measurable business value (e.g., improved decisioning, automation, personalization)
• Ensure solutions are aligned to real-world workflows and deliver tangible outcomes
• Lead the development and evolution of enterprise ontologies, taxonomies, and data models that represent key business concepts and relationships
• Establish a scalable semantic layer that enables reuse across analytics, AI, and operational use cases
• Ensure alignment of definitions across domains to reduce fragmentation and duplication
• Support both structured and unstructured data integration, enabling downstream AI and analytics applications
• Ensure data is discoverable, understandable, and usable across teams through consistent definitions and relationships
• Partner deeply with business domains to understand workflows, decision points, and data dependencies, acting as a product owner for how data supports those functions
• Own the end-to-end product lifecycle from discovery and definition to delivery and iteration
• Define product requirements, success metrics, and release plans
• Continuously refine the semantic data product based on user feedback and evolving business needs
• Define critical metrics to measure adoption, usability, and impact of the semantic data product
• Supervise usage and continuously improve accessibility and value delivery
• Evangelize the value of connected semantic data across the organization
• Partner with data governance teams to establish standards, definitions, and data quality expectations
• Promote consistency, reusability, and scalability of data assets across the enterprise

Senior Manager, AI Lead – Semantic Layer

• analyze complex problems and provide strategic insights
• mentor and guide junior associates in their roles
• maintain rigorous standards of quality and compliance in deliverables
• navigate complex situations to achieve project goals
• support enterprise-scale transformation initiatives for clients

Senior Associate – AI Engineering

• work to design, deploy, and optimize Generative AI solutions
• build agentic workflows and RAG systems
• apply latest advancements in Foundation Models to create AI-native tools
• build and refine LLM-powered features and autonomous agents
• design and implement RAG pipelines to connect proprietary data with LLMs
• explore and eliminate GTM friction points by rapid prototyping AI solutions
• integrate AI workflows into modern sales stack (e.g., Salesforce, Gong)
• develop and maintain production-grade infrastructure for AI orchestration

AI Engineer – Go To Market

• Support the Product Manager in refining and executing the agentic AI digital media platform strategy
• Own end-to-end delivery of AI-powered features from concept to production
• Design new intuitive user experiences powered by intelligent automation
• Partner with engineering to define AI system behavior and interaction patterns
• Translate new business workflows into scalable product functionality
• Improve evaluation metrics for AI-driven features (accuracy, reliability, latency, ROI impact)
• Lead experimentation to improve AI-driven feature performance
• Improve human-in-the-loop review and escalation workflows
• Collaborate with engineering to implement APIs and integrations
• Support stakeholder analysis to understand user behavior and requirements
• Improve user testing programs to validate assumptions
• Contribute to product roadmap definition and own detailed roadmap execution and prioritization
• Translate business requirements into product specifications and user stories
• Manage backlog, sprint planning, and delivery cadence
• Prioritize initiatives based on ROI and technical feasibility
• Collaborate with sales, marketing, and customer success for launches
• Monitor product performance and drive continuous improvement

Product Owner – Agentic AI Platform

• Design and implement scalable APIs for agent orchestration and learner interaction
• Build data pipelines that feed agent evaluation and continuous improvement cycles
• Create event streaming infrastructure for real-time learner interaction analysis
• Ensure data quality and accessibility for training and evaluating learning agents
• Improve data infrastructure for cost and performance in close coordination with Data and ML teams
• Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles
• Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems
• Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows)
• Participate in on-call rotation and incident response for learning platform reliability
• Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model

Senior Software AI Engineer

• Own the end-to-end administration of the Association’s primary AI platform and Microsoft Copilot
• Manage user provisioning and deprovisioning, SSO configuration, permission tiers, and seat allocation across standard and Pro/Max license tiers
• Ensure platform configurations meet enterprise security standards and Responsible AI guidelines
• Design, build, and maintain integrations between AI platforms and core enterprise systems
• Partner with Enterprise Risk to ensure all AI platform configurations meet data security policies
• Track platform consumption patterns and generate usage analytics
• Serve as the primary technical point of contact with AI platform vendors
• Lead technical due diligence when new AI tools are proposed
• Own the build and maintenance of internal CoE tooling
• Forecast platform capacity needs as adoption grows

Director of AI Platform Operations

• Develop comprehensive systems and frameworks for AI applications and products.
• Construct prototypes and minimum viable products to validate AI/ML solutions before committing substantial resources.
• Develop, refine, and productionize AI systems using cloud resources such as Azure OpenAI, Azure ML, and Cognitive Services.
• Contribute to the creation of robust data pipelines for model evolution and production systems.
• Develop specifications for low latency APIs and services necessary to deploy AI models and incorporate them into applications.
• Supervise and sustain AI systems in production to guarantee consistent model accuracy and reliability.
• Actively participate in a team that exercises principled, agile-like development practices.
• Create and maintain thorough documentation that is consistent with team procedures, corporate policies, and expectations.
• Ensure peer review on all assigned work, as well as conduct peer reviews over the work of others as requested.
• Guide junior AI engineers on AI/ML and industry best practices and methodologies.
• Keep abreast of new tools and concepts through reading documentation or literature and actively practicing skills development.
• Support and participate in meetings with external stakeholders.

AI Engineer – III

• Design and implement AI-powered customer support solutions (chatbots, virtual agents, agent assist tools)
• Build and maintain systems for automating ticket triage, classification, routing, and response generation
• Develop retrieval-augmented generation (RAG) systems using internal knowledge bases and documentation
• Integrate AI into support platforms (e.g., Salesforce Service Cloud, etc.)
• Analyze workflow data (tickets, chats, call transcripts) to identify automation opportunities and insights
• Optimize AI systems for accuracy, latency, cost, and customer satisfaction
• Establish evaluation frameworks (e.g., response quality, resolution rate, deflection rate)
• Collaborate with services and support teams to continuously refine workflows and ensure practical usability
• Implement guardrails and monitoring to ensure safe, reliable, and compliant AI behavior

AI Developer – Customer Services and Support

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• 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.
• 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.

Senior Partner AI Architect

• 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.
• Leverage expert-level knowledge of Genesys AI capabilities to articulate and demonstrate product value to customers and prospects.
• Support pre-sales activities such as technical discovery, solution design, product demonstrations, sandbox/trial engagements, AI integration guidance, and value assessments.
• Design and deploy AI prototypes in sandbox and/or customer development environments to validate use cases, integrations, latency, and success criteria , and to highlight the differentiated value of Genesys AI.
• Partner with account teams and Professional Services to transition successful prototypes into production pilots supporting technical handoff, hardening, KPI alignment, and business outcome validation.
• Leverage expert-level knowledge of Genesys AI capabilities to articulate and demonstrate product value to customers and prospects.
• Develop reusable technical assets and enable Solution Consultants, partners, and account teams through workshops, 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.
• Influence CIO/CTO-level stakeholders and position Genesys as integral to an organization’s broader IT and transformation strategy.

Senior AI Architect

• Analyze and model organizational data for AI function to draw business insights
• Apply data extraction, transformation and loading techniques for large data sets
• Develop LLM strategies and enhance AI ML model deployment efficiency
• Design and develop custom ML, Gen AI, NLP, LLM Models for AI ML pipelines
• Work closely with MLOps team to create and maintain evaluation solutions for models
• Collaborate with product teams and stakeholders for deployment in production environments
• Ensure adherence to standards, governance, and best practices in ML model development

Gen AI Engineer

• As a Senior AI Engineer focused on Agentic Evaluation and Verification and Validation (V&V), you will join the AI and Data Science team within Slingshot’s Research and Development organization.
• You will contribute to advancing how intelligent systems are evaluated and validated for mission-critical space operations.
• This role focuses on building and scaling evaluation frameworks, benchmarks, and simulation-backed validation systems for agentic AI systems, including multi-step, tool-using, and autonomous decision-making workflows powered by LLMs and reinforcement learning.
• Your work will directly support the development of reliable and trustworthy autonomous mission planning systems.
• You will partner closely with AI researchers and domain experts to translate real-world mission concepts into structured, testable evaluation systems.

Senior AI Engineer, Agentic Evaluation – V&V

• Embed directly with clients to gather requirements, translating ambiguous operational challenges into concrete technical architectures and delivering high-impact, AI-driven solutions with extreme ownership.
• Train and deploy custom AI agents to prevent fraud and automate complex fincrime workflows across a diverse portfolio of use cases.
• Utilize advanced prompt engineering, fine-tuning, and rigorous evaluation frameworks to continuously optimize AI agent performance, ensuring high accuracy, safety, and reliability.
• Champion an AI-first approach across internal teams and client engagements, demonstrating the tangible business impact of advanced machine learning interventions.
• Establish and evolve our AI risk metrics, the supporting datasets, and the evaluation pipelines used to measure the causal impact of deployed agents.
• Collaborate tightly with internal engineering and product teams to scale custom models into production, ensuring seamless integration and optimized operational performance.

Forward Deployed AI Engineer

• Oversee the design and architecture of scalable, reliable, and secure systems that power autonomous detection, RAG-backed investigation, and auto-remediation workflows
• Define the technical strategy and architecture for our core platform, ensuring it scales to petabytes of security data and billions of daily events
• Power autonomous detection, RAG-backed investigation, and auto-remediation workflows
• Develop and Productionize large-scale LLMs, graph-based reasoning engines, and streaming feature pipelines that operate on billions of security events
• Own Evaluation & Reliability of AI systems—from prompt libraries and fine tuning to red-team testing, latency budgets, and fallback strategies
• Oversee cross-cutting concerns like observability, reliability, performance, security, and operational excellence in production environments
• Partner tightly with Product, Detection Engineering, and Customer Success to translate real-world attacker behavior into robust ML and rule-based detections, and define the technical roadmap
• Experiment with retrieval-augmented generation, tool-calling agents, and multi-modal models (text + logs + graphs) to maintain a competitive advantage
• Mentor and influence engineering teams on best practices in cloud architecture, reliability, and security-first development.

See all jobs on JobTailor

AI Architect

Senior AI Engineer

AI Engineer Manager

Senior Manager, AI Lead – Semantic Layer

Senior Associate – AI Engineering

AI Engineer – Go To Market

Product Owner – Agentic AI Platform

Senior Software AI Engineer

Director of AI Platform Operations

AI Engineer – III

AI Developer – Customer Services and Support

AI Engineer

Senior Partner AI Architect

Senior AI Architect

Gen AI Engineer

Senior AI Engineer, Agentic Evaluation – V&V

Forward Deployed AI Engineer

Product Owner – Agentic AI Platform

AI Architect