AI Platform Engineer (Python, AWS)

Be 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.

Summary

The AI Platform Engineer contributes hands-on execution for Genesys's enterprise AI tool ecosystem — including Claude, ChatGPT Enterprise, Cursor, Amazon Q, and internal platforms such as GEL. You help keep these tools available, secure, and well-integrated for thousands of internal users, working through established runbooks and processes while building the technical depth to take on broader ownership over time.

You will work closely with senior engineers and the Manager, AI Enablement & Automation on access governance, monitoring, and integration work, growing your platform engineering skills on a team building the tools that power AI adoption across the business.

What You Will Do

AI Platform Application Development

Design, develop, test, deploy, and operate Python-based services and automation supporting AI governance, security, privacy, and compliance requirements.

Build tooling for AI application inventory, platform onboarding, risk classification, access reviews, policy compliance, control testing, exception management, approval workflows, and audit-evidence collection.

Translate governance policies and security requirements into enforceable technical controls, automated checks, alerts, and measurable control outcomes.

Develop security and compliance checks for sensitive-data exposure, inappropriate access, policy violations, secrets leakage, unsafe tool use, and other AI-specific risks.

Partner with Security, Privacy, Legal, Compliance, and Internal Audit to define evidence requirements and automate recurring evidence collection and reporting.

Build AI-enabled internal applications and workflows using enterprise model APIs and SDKs, structured outputs, function or tool calling, prompt and configuration versioning, and human-in-the-loop approval patterns.

Implement model and workflow evaluations, regression tests, guardrails, fallback behavior, rate limits, timeout handling, and cost controls.

Build dashboards for platform health, license utilization, token and cost consumption, access posture, policy exceptions, security events, control effectiveness, and audit readiness.

Enable successful platform onboarding for internal customers and operationally support their success.

AI Platform Operations Support

Monitor uptime and health across enterprise AI tools (Claude, ChatGPT Enterprise, Cursor etc.), following established runbooks to detect and escalate degradations, and assist with resolution alongside senior engineers.

Execute routine platform configuration changes and API version updates using documented change procedures, minimizing disruption to end users.

Maintain monitoring and alerting dashboards, flagging coverage gaps or false positives to senior engineers to keep runbooks accurate.

Access & Governance Execution

Administer day-to-day identity and access management tasks for the AI tool portfolio — provisioning, de-provisioning, and role assignments — following corporate security policy.

Support periodic access reviews and compile evidence for compliance reporting and internal audits.

Partner with Security and Compliance teams on routine data-handling and governance checks, escalating exceptions to senior team members.

Integration & Support Contribution

Build and maintain straightforward integrations between AI platforms and internal workflows (e.g., SSO connections, basic API connectors).

Serve as a point of contact for access, integration, and configuration issues, resolving routine cases and escalating complex ones.

Track usage and adoption metrics for assigned platform integrations, feeding accurate data into the team's reporting dashboards.

What You Bring

We encourage candidates who meet many, though not all, of the qualifications below to apply.

Required Qualifications

2+ years of experience in software engineering, platform engineering, IT operations, or DevOps, with some exposure to SaaS/AI tooling administration.

Strong proficiency in Python for production application and automation development, including REST services and clients, data processing, package management, type checking, automated testing, structured logging, exception handling, and secure configuration management.

Hands-on experience designing and integrating REST APIs, webhooks, and event-based workflows, including authentication, authorization, rate-limit handling, retries, schema validation, and API-version management.

Hands-on experience building AI-enabled applications or workflows using one or more enterprise model APIs or SDKs.

Understanding structured model outputs, function or tool calling, prompt/configuration versioning, human-in-the-loop controls, model evaluations, guardrails, and AI application observability.

Proficiency in SQL and experience designing data models, pipelines, queries, and dashboards for operational, security, compliance, cost, or usage reporting.

Experience with Git, pull requests, code review, automated testing, and CI/CD pipelines.

Working knowledge of identity and access management concepts (provisioning, role-based access, audit logging) for cloud-based platforms.

Familiarity with monitoring and alerting tools (Datadog, PagerDuty, CloudWatch, or similar).

Experience with monitoring, alerting, and observability tools such as Datadog, CloudWatch, Splunk, Grafana, OpenTelemetry, or equivalent.

Clear written and verbal communication, and comfort following documented runbooks and escala