[Hiring] Senior AI Platform Engineer @Genesys

Role Description

The Senior AI Platform Engineer shapes the reliability, security, and scalability of the enterprise AI ecosystem that thousands of Genesys employees rely on to work more effectively. This role owns critical capabilities across platforms including Claude, ChatGPT Enterprise, Cursor, Amazon Q, and internal AI services, turning rapidly evolving AI technologies into dependable enterprise services with strong operational and governance foundations.

You will operate at the intersection of AI platform engineering, automation, reliability, identity, and governance. You will influence strategy across Engineering, Security, Privacy, Legal, Compliance, Internal Audit, and business teams, translating enterprise requirements into scalable technical controls, integrations, and self-service capabilities.

Your work will enable employees to use AI safely and effectively while improving platform availability, operational efficiency, cost visibility, and audit readiness. You will also help advance Genesys Cloud and the broader company AI strategy by building reusable engineering patterns that reflect empathy for users, strong collaboration across teams, and ambitious thinking about how AI can improve customer and employee experiences.

The role provides exposure to enterprise AI architecture, agentic systems, platform governance, and organization-wide AI initiatives, with opportunities to expand technical leadership and influence platform direction as adoption and capabilities grow.

Key Responsibilities

• Own the end-to-end engineering and production reliability of Python services, APIs, automation, and integrations that support AI platform administration, governance, security, privacy, and compliance.
• Design scalable AI-enabled applications and agentic workflows using enterprise model APIs and SDKs, structured outputs, tool calling, Model Context Protocol (MCP), retrieval-augmented generation (RAG), evaluations, guardrails, and human approval patterns.
• Drive the evolution of AI governance tooling for application inventory, onboarding, risk classification, access reviews, policy compliance, control testing, exception management, approval workflows, and automated audit evidence.
• Establish reusable engineering patterns for prompt and configuration versioning, regression testing, fallback behavior, rate limiting, timeout handling, observability, and cost controls that improve platform consistency and resilience.
• Lead production reliability across enterprise AI tools, using SLOs, monitoring, alerting, incident response, runbooks, and post-incident improvements to maintain dependable service for thousands of internal users.
• Plan platform upgrades, configuration changes, model and vendor API migrations, and production releases with clear testing, rollout, rollback, and stakeholder communication strategies.
• Instrument AI applications and platforms to measure latency, errors, token consumption, output quality, adoption, license utilization, security events, policy exceptions, and cost, converting telemetry into platform and procurement decisions.
• Design and automate identity and access controls using SSO, SAML, OIDC, SCIM, role-based access control, least privilege, and audit logging across the enterprise AI portfolio.
• Implement technical safeguards for AI-specific risks including prompt injection, sensitive-data disclosure, secrets leakage, insecure output handling, excessive agency, unsafe tool execution, and supply-chain risks.
• Drive alignment between Engineering, Security, Privacy, Legal, Compliance, and Internal Audit by translating governance requirements into enforceable controls and measurable evidence for frameworks including SOC 2, ISO 27001, and ISO/IEC 42001.
• Evaluate new AI platforms, MCP integrations, connectors, plugins, AI gateways, and emerging capabilities to inform architecture, adoption, consolidation, and investment decisions.
• Develop self-service provisioning, internal APIs, SDKs, infrastructure as code, and workflow integrations that reduce manual administration and enable scalable, API-driven AI operations.
• Resolve complex Tier 2 and Tier 3 platform issues while maintaining architecture documentation, technical runbooks, and operational playbooks that strengthen support capabilities across the broader team.
• Partner with enablement teams and senior stakeholders to turn operational insights into guidance, training, and platform improvements that increase the safe and effective adoption of AI across Genesys.

Qualifications

• 5+ years of experience in platform engineering, IT operations, DevOps, or a related engineering discipline, including at least 2 years supporting SaaS or AI tooling at enterprise scale.
• Advanced proficiency in Python for production services and automation, including REST services and clients, asynchronous programming, package management, type checking, automated testing, structured logging, exception handling, and secure secrets management.
• Proven experience designing, building, and operating REST APIs, webhooks, and event-driven workflows using OAuth 2.0 or OIDC, authorization controls, rate-limit handling, retries, idempotency, schema validation, and API version management.
• Hands-on experience delivering production AI applications or agentic workflows using enterprise model APIs or SDKs such as Anthropic, OpenAI, Amazon Bedrock, or Azure OpenAI, including structured outputs, tool calling, retrieval, evaluations, and guardrails.
• Strong experience with enterprise identity and access management, including SSO, SAML, OIDC, SCIM provisioning, role-based access control, least privilege, and audit logging with platforms such as Okta or Microsoft Entra ID.
• Experience with cloud infrastructure, preferably AWS, containers, and infrastructure as code using Terraform, CloudFormation, or equivalent technologies.
• Proficiency in SQL and experience designing data models, pipelines, queries, and dashboards for operational, security, compliance, cost, or usage reporting.
• Demonstrated experience owning production reliability through SLOs, monitoring, alerting, on-call participation, incident management, and post-incident reviews using technologies such as Datadog, CloudWatch, Splunk, Grafana, PagerDuty, or OpenTelemetry.
• Strong software engineering practices using Git, pull requests, code review, automated testing, CI/CD pipelines, and secure development practices.
• Clear written and verbal communication skills, with demonstrated ability to produce technical design documents, architecture documentation, runbooks, and stakeholder updates.

Preferred Qualifications

• Experience integrating enterprise AI platforms with identity providers such as Okta or Microsoft Entra ID through SSO and SCIM.
• Familiarity with AI and machine learning platform concepts including API rate limiting, token-based billing, prompt management, model versioning, evaluations, and cost controls.
• Experience building or operating MCP servers, AI gateways, LLM proxies, or agent frameworks, including connector permissions and tool-access scopes.
• Experience with vector databases, knowledge bases, embedding pipelines, and retrieval-quality evaluation for RAG applications.
• Strong background in enterprise architecture or systems integration within a mid-sized or large technology organization.
• Relevant cloud, identity, or security certifications, such as AWS, Okta, or CCSP.

Benefits

• 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.
• Join a global team of nearly 7,000 employees with flexible ways of working designed to help people do their best work.
• Build future-ready skills through mentorship, learning programs, leadership development and education support.
• Benefits include paid volunteer time, August Free Fridays, well-being resources and regionally tailored programs for employees and their families.
• Genesys is Great Place to Work® certified in 17 countries and 94% of employees are proud to tell others they work at Genesys.