Posted today · be early

AI Platform Engineer (Python, AWS)

Genesys

IndiaremotePosted today

Skill Required

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Key highlights

Required experience: 2+ years in software/platform engineering, IT ops, or DevOps
Key Benefit: Flexible-first culture and August Free Fridays
Notable Requirement: Strong proficiency in Python for production automation
Core Focus: AI Platform operations and SaaS administration

Role overview

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.

Responsibilities

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.
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.
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.
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.

Requirements

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 escalating appropriately.

Nice to have

Exposure to one or more enterprise AI platforms (ChatGPT Enterprise, Claude, Microsoft Copilot, Amazon Q, Cursor).
Familiarity with SSO/SCIM concepts and identity providers (Okta, Azure AD).
Experience integrating with platforms such as Okta or Microsoft Entra ID, ServiceNow, Splunk, Datadog, Snowflake, cloud security tools, GRC platforms, or DLP systems.
Familiarity with AI-specific security risks such as prompt injection, sensitive-data disclosure, insecure output handling, excessive agency, model abuse, unsafe tool execution, and supply-chain risks.
Familiarity with AI governance and information-security frameworks such as NIST AI RMF, ISO/IEC 42001, ISO 27001, SOC 2, and privacy-by-design principles.
Experience with LLM evaluation, AI red teaming, model or agent observability, prompt lifecycle management, or guardrail platforms.

Benefits

Flexible-first culture with flexible ways of working.
Growth opportunities through mentorship, learning programs, leadership development and education support.
Paid volunteer time.
August Free Fridays.
Well-being resources and regionally tailored programs for employees and their families.

Additional details

Key Skills: AI platform operations and SaaS administration fundamentals, Identity and access management (IAM) execution, Monitoring and alerting support, Scripting and automation, Clear documentation and communication.
Business Impact: Keeps enterprise AI tools reliable and accessible day-to-day for thousands of employees; Reduces compliance risk by executing access governance and audit tasks; Builds foundational platform engineering capability.
Interview Process: Talent Acquisition review, Zoom interview with TA Partner, meeting with hiring manager and interview team, maximum of five interviews typically.
Company Info: Genesys helps organizations create better customer experiences through AI-powered experience orchestration; platform connects people, systems, data and AI.
Equal Opportunity Employer: Evaluates 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.