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.

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