AI Engineer

• Design, build, and deploy AI-powered tools and automations for HR and Talent use cases, including intelligent workflow automations, employee-facing chatbots, document processing solutions, and decision-support systems
• Identify opportunities to improve manual or repetitive HR workflows through scalable AI/ML-driven automation solutions
• Develop integrations between AI solutions and enterprise systems including Workday, ATS platforms, ServiceNow Ticketing, and other internal tools ensuring seamless data flow and user experience
• Partner directly with HR Operations and cross-functional stakeholders to gather requirements, prototype solutions, run pilots, and drive successful adoption
• Continuously monitor and improve deployed tools based on user feedback, operational metrics, and evolving business needs
• Create technical documentation, implementation guides, and operational handoff materials to support long-term maintainability and scalability.

Principal AI Engineer, HR Automation

• Design, develop, and deploy AI, machine learning, and generative AI solutions
• Partner with the CIO and stakeholders to identify, prioritize, and scope AI/ML/GenAI use cases
• Prototype and deliver proofs-of-concept for emerging AI technologies
• Translate validated proofs-of-concept into clear functional and non-functional requirements
• Build and maintain semantic/domain models
• Develop AI-enabled applications and demos using Streamlit and APIs
• Implement solutions that leverage AWS services
• Collaborate with BI, Architecture, and Security teams
• Develop and implement automation solutions for the QA department
• Create and maintain detailed documentation for AI models, processes, and workflows

Senior AI Developer – Engineer

• Design and deploy end-to-end agentic AI workflows using planner–worker, orchestrator–executor, multi-agent, and RAG-based architectures
• Build reusable components and reference patterns for tool routing, state management, error handling, and human-in-the-loop checkpoints
• Implement robust retrieval pipelines (hybrid search, vector + keyword, graph-aware retrieval) over technical documents, historian data, and operational records
• Translate domain problems from drilling, completions, production, geophysics, and power plant operations into well-scoped agentic use cases with clear success metrics
• Build and maintain a semantic layer over our data lake and warehouse using Snowflake Semantic Views and Databricks Unity Catalog Metric Views, making business concepts queryable by both humans and agents
• Develop and curate knowledge graphs that connect domain entities (wells, pads, assets, equipment, events, documents) and serve as grounding context for LLM reasoning
• Standardize how agents access enterprise data through Model Context Protocol (MCP) servers and equivalent integration patterns
• Establish agent evaluation frameworks including golden datasets, automated regression tests, and structured evals for accuracy, faithfulness, and tool-use correctness
• Implement tracing, logging, and observability across agent runs to support debugging, cost monitoring, and continuous improvement
• Build feedback loops that capture user input and convert it into eval cases and prompt/system improvements
• Support production incidents and platform-level issues impacting deployed agents
• Deploy agents as production services on our Azure-native stack (App Service, Container Apps, Functions) with Entra ID SSO, Key Vault-managed secrets, and proper cost controls
• Build lightweight UIs (Streamlit, Gradio, or React) for agentic applications and internal tools
• Lead design reviews and cross-functional enablement sessions on agentic AI patterns and best practices

• Build and maintain backend services that support agent execution, orchestration, and lifecycle management.
• Develop systems for workflow coordination, state management, and tool integration.
• Write high-quality, maintainable code in Python to power platform capabilities and APIs.
• Deploy and operate services on Kubernetes, ensuring reliability and scalability.
• Contribute to systems for observability, logging, tracing, and debugging of distributed workflows.
• Improve system performance across latency, throughput, and fault tolerance.
• Build internal tools and APIs that improve the developer experience for teams using the platform.
• Collaborate with engineers, product managers, and AI teams to deliver production-ready solutions.
• Participate in design discussions and contribute to system architecture and technical decisions.

Software Engineer, Senior Software Engineer – AI Platform

• Partner with the AI Process Lead and engineering teams to execute prioritized AI productivity initiatives
• Embed with teams to understand day-to-day coding workflows and identify practical improvement opportunities
• Create and maintain team-level AI artifacts, including AGENTS.md, .github/copilot-instructions.md, reusable prompts, and workflow playbooks
• Build and refine AI personas for concrete use cases (for example: PR reviewer, test gap detector, refactoring assistant)
• Implement AI-assisted code review and developer workflow automations (GitHub, CI/CD, scripts, internal tooling)
• Prototype and deliver tools in Python (or similar), with clear documentation and adoption guidance
• Track and report impact metrics for implemented changes (adoption, cycle time, review quality, defect trends)
• Provide enablement support through demos, office hours, and team training sessions

AI Engineer – Developer Productivity

• Assess how engineering teams are currently using AI tools and identify high-value improvement opportunities.
• Evaluate AI-assisted workflows using measures such as quality, speed, adoption, and cost efficiency.
• Build lightweight instrumentation, analysis, dashboards, or automation tools to measure AI usage patterns and support optimization experiments.
• Partner with engineering leads and individual contributors across multiple teams to drive adoption and behavior change.
• Present findings, proposals, and results to engineering leadership; create clear, compelling documentation and slide decks.
• Stay current on model capabilities, pricing, workflow patterns, and optimization techniques.

AI Engineer – AI Efficiency, Economics

• Assess how engineering teams are currently using AI tools and identify high-value improvement opportunities.
• Evaluate AI-assisted workflows and recommend changes that improve outcomes and reduce waste.
• Build lightweight instrumentation, analysis, dashboards, or automation tools to measure AI usage patterns.
• Partner with engineering leads and individual contributors across multiple teams to drive adoption and behavior change.
• Present findings, proposals, and results to engineering leadership; create clear, compelling documentation and slide decks.
• Stay current on model capabilities, pricing, workflow patterns, and optimization techniques.

• Partner with the AI Process Lead and engineering teams to execute prioritized AI productivity initiatives
• Embed with teams to understand day-to-day coding workflows and identify practical improvement opportunities
• Create and maintain team-level AI artifacts, including AGENTS.md , .github/copilot-instructions.md , reusable prompts, and workflow playbooks
• Build and refine AI personas for concrete use cases (for example: PR reviewer, test gap detector, refactoring assistant)
• Implement AI-assisted code review and developer workflow automations (GitHub, CI/CD, scripts, internal tooling)
• Prototype and deliver tools in Python (or similar), with clear documentation and adoption guidance
• Track and report impact metrics for implemented changes (adoption, cycle time, review quality, defect trends)
• Provide enablement support through demos, office hours, and team training sessions

Senior AI Engineer

• Turn quantitative prototypes into reusable tools, services, packaged desktop applications, interfaces, and workflow products that can move from internal demo to client pilot to scaled offer.
• Ship across both cloud-hosted services and locally distributed desktop applications, including Electron-based apps when the workflow or client environment calls for it.
• Build enterprise hardening into the productization layer, including authentication, role-based access control (RBAC), observability, security, release quality, cost controls, and deployment discipline.
• Build evaluation, regression, and release discipline into the productization layer so model logic and agent behavior remain measurable as systems change.
• Work closely with the quant lead so model logic, evaluation intent, and governance requirements survive the move into production.
• Make pragmatic architecture choices across large language models (LLMs), deterministic rules, and hybrid systems based on value, latency, cost, and reliability.
• Help shape repeatable build patterns so strong prototypes become faster, more reliable, and more reusable over time.

QuantAI Engineer – Trading, Investment & Optimization

• 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

• Design, develop, and deploy AI solutions
• Build and enhance AI-powered applications
• Help transition AI solutions from prototype to production
• Develop and manage prompt strategies and templates
• Collaborate with data engineers to integrate AI solutions with governed data sources
• Participate in evaluation, learning & governance awareness

AI Engineer I

• Design, build, and iterate on AI Assistants integrated with enterprise tools including Jira, Slack, and internal data sources
• Develop and maintain prompt engineering frameworks, evaluation pipelines, and feedback loops to continuously improve assistant quality and reliability
• Build and maintain APIs, backend services, and integrations that connect AI capabilities to the tools teams already use
• Collaborate with AI Ops, security, and platform engineering teams to ensure systems are production-grade, secure, and observable
• Contribute to a repeatable framework for building new AI Assistants as the program expands
• Evaluate new models, tools, and libraries and make informed recommendations on when and how to adopt them
• Identify and mitigate risks related to AI outputs, data handling, and responsible use
• Contribute to technical documentation, runbooks, and onboarding materials that help the team scale what gets built

Staff AI Engineer

• 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 scalable, secure enterprise AI and GenAI architectures aligned to business strategy
• Lead solutioning and hands‑on prototyping of AI systems, including LLM‑based workflows
• Translate business needs into architecture diagrams, solution designs, and integration patterns
• Partner with data, platform, security, and application teams to embed AI into core enterprise systems
• Support delivery teams with implementation guidance, architectural tradeoffs, and best practices
• Contribute to enterprise AI platform development, reusable patterns, and standards
• Apply AI governance, risk, and security guardrails across all solutions
• Engage with external AI partners and assist in evaluating proof‑of‑value initiatives
• Present architectures and solution designs for enterprise review and alignment

Senior AI Architect

• 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

• Communicate effectively with platform architects, developers, data engineers, product owners, enterprise cloud teams, and application teams.
• Prepare and manage Azure subscriptions for platform and team use by applying naming conventions, tagging standards, baseline configurations, and environment readiness controls.
• Build and maintain Dev, UAT, and Prod cloud environments based on defined standards, ensuring consistency across environments over time.
• Create and maintain Azure resource group structures per application, product, or platform service, including implementation of team access boundaries and ownership models.
• Implement and maintain RBAC assignments at subscription and resource group scope, updating access as teams onboard or requirements evolve.
• Configure and maintain service principals, managed identities, Key Vault access, and secure system-to-system permissions across platform services and pipelines.
• Implement Azure networking changes including VNets, subnets, network security groups, route tables, private endpoints, DNS-related configuration, and cross-region VNet peering as defined by architecture.
• Establish and support secure connectivity to Databricks, platform services, and shared cloud components across regions.
• Deploy, maintain, monitor, and troubleshoot shared platform services such as AKS, Azure Container Apps, Foundry environments and integrations, GitLab runners, Key Vaults, API Management, Application Gateway, and ingress components.
• Create and maintain GitLab projects and group structures, configure CI/CD pipelines, variables, permissions, and integrations between infrastructure outputs and application deployment pipelines.
• Execute infrastructure deployments across Dev, UAT, and Prod, troubleshoot deployment failures, resolve configuration drift, and maintain repeatable deployment patterns.
• Support Databricks integration activities including secure networking, access configuration, supporting integrations, data connectivity validation, and operational issue resolution.
• Monitor health and availability of platform services, application environments, pipelines, and infrastructure components; act to return systems to health when alerted.
• Resolve day-to-day Azure operational issues including access problems, networking failures, deployment errors, pipeline failures, configuration inconsistencies, and onboarding blockers.
• Support application and product teams as they onboard to the platform, including access setup, pipeline enablement, environment readiness, and operational guidance.

Senior Data & AI Platform Cloud Operations Developer

• 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, 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.

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