Technical mission

Build, scale, and maintain production delivery platforms and infrastructure for AI Agents, LLM workflows, and Model Context Protocol (MCP) servers. Establish repeatable evaluation gates, enforce sandboxed agent execution, enable zero-downtime rollouts, and ensure high-availability runtime operations for non-deterministic AI workloads.

Key Responsibilities

• Deploy, host, scale, and operationalize AI Agent frameworks, vector stores, and Model Context Protocol (MCP) servers across Kubernetes clusters.
• Design CI/CD pipelines for Python services, agent definitions, prompts, tools, MCP servers, and platform configuration.
• Implement GitOps-based Kubernetes deployments with promotion, rollback and environment-specific policy controls.
• Integrate automated unit, API, security and LLM-evaluation checks (e.g., prompt stability, tool-use accuracy, output safety, latency/cost regression) into release gates.
• Implement secrets handling, dynamic sandbox isolation for agent execution, and least-privilege deployment identities.
• Instrument pipelines and deployed services for agent-level observability (token usage, tool invocation tracking, LLM latency, cost monitoring, distributed tracing), health monitoring, and incident diagnosis.
• Maintain reusable deployment templates for services, agents, and MCP tools, and coach engineering teams on release standards.

Required Technical Capabilities

• Minimum experience: 9+ years in DevOps, SRE or platform engineering, including 3+ years with production Kubernetes, GitOps, and infrastructure as code, combined with hands-on production experience deploying AI Agents, LLM infrastructure, or MCP servers.
• Hands-on experience deploying, managing, and scaling AI-driven solutions, vector databases, proxy gateways, or MCP servers in production.
• Strong CI/CD and GitOps experience for Kubernetes-hosted services.
• Terraform or equivalent infrastructure-as-code and practical cloud operations experience, preferably Azure.
• Python scripting for pipeline tooling, release checks, operational automation, and integrating AI evaluation SDKs.
• Secrets management, container security, workload-isolation practices, and dynamic agent sandboxing.
• Monitoring, alerting, and telemetry for both delivery pipelines and runtime services (including LLM/Agent tracing tools like LangFuse, Phoenix/Arize, OpenTelemetry, Datadog, or Prometheus).
• Experience making automated test and LLM evaluation results enforceable in CI.

Preferred Experience

• Release strategies such as canary, blue/green and progressive delivery for agent prompt changes and LLM versioning.
• LLM or agent evaluation frameworks (e.g., DeepEval, Ragas, LangSmith) and benchmark execution in CI.
• Software supply-chain controls, artifact signing and dependency/security scanning.

Expected deliverables / acceptance evidence

• Reusable CI/CD templates for services, agents, MCP tools, and framework releases.
• Automated quality, security and LLM-evaluation gates.
• GitOps deployment definitions with tested rollback paths.
• Release observability, AI tracing, audit trail and operational documentation.

Primary Interfaces

Works with Infrastructure, Agent Runtime, QA Automation, AI Test & Dataset Engineering and service owners across all pods.

What We Have For You

• Great compensation package, medical benefits for you and your family, free lunch, annual performance-tied increments, performance recognition awards, and a great lean and agile work culture!
• Convo endorses a culture of diversity in all aspects and aims to build a diverse team of amazing individuals!