Senior AI Platform Engineer

Role: Senior AI Platform Engineer Location: Hybrid (Richardson , TX) Duration FTE Salary:150-170 K Qualifications Required Experience 8-10 years of software engineering experience, including at least 7 years building and operating production systems in cloud environments and hands-on experience deploying AI/ML services into production. 2-3 years of experience utilizing AI-assisted development tools (for example, Claude Code, Codex, Cursor, GitHub Copilot, or similar) to accelerate platform development, deployment, testing, troubleshooting, and documentation. Strong experience with AWS, Eligible to workP, Azure, hybrid cloud, or on-premises infrastructure; Docker, Kubernetes, GitHub Actions or equivalent CI/CD tooling, Terraform, and Helm. Working knowledge of authentication and authorization technologies, including OAuth 2.0, OpenID Connect (OIDC), SAML, JWT, Role-Based Access Control (RBAC), and Identity and Access Management (IAM). Experience implementing workload identity, service-to-service authentication, and securing API and tool access. 4+ years of scripting and automation experience, preferably with Python and JavaScript, including troubleshooting across Linux, containers, Kubernetes, networking, and distributed production systems. Ability to architect secure and cost-efficient hosting solutions for open-source large language models (LLMs) in cloud, private cloud, or on-premises environments. Preferred Qualifications Experience with agentic AI frameworks (e.g., LangGraph, Google ADK), agent-to-agent communication patterns, tool integration, workflow orchestration, Retrieval-Augmented Generation (RAG), AI evaluation frameworks, and guardrail design. Familiarity with AI observability and tracing platforms (e.g., LangSmith, Grafana/LGTM), GPU infrastructure for model serving, and orchestration platforms such as Dagster, Prefect, or Apache Airflow. Strong communication, mentoring, stakeholder management, and project planning skills. Ability to support critical production deployments and incident response activities when necessary. Success Metrics Reusable deployment patterns are documented, standardized, automated, observable, and auditable across AI and agent-based workloads. Development, testing, and production release processes become more efficient, secure, consistent, and easier to govern. Security, compliance, governance, observability, and responsible automation practices are embedded throughout the software delivery lifecycle. Disclaimer: Diverse Lynx LLC is an Equal Opportunity Employer. All applicants and employees are evaluated without discrimination, based solely on their qualifications, ability, competence and performance. This email and its attachments may contain confidential or proprietary information and is intended only for the recipient(s). If you received this message in error, please disregard it and notify the sender. If you no longer wish to receive our communications, you may unsubscribe at any time. Security Notice : Our official website is We do not operate or authorize any other websites representing Diverse Lynx LLC. Diverse Lynx