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 responsibilitiesDeploy, 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.
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.
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.
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.
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!
Skills Required
- 9+ years of experience in DevOps, SRE, or platform engineering
- 3+ years of production experience with Kubernetes, GitOps, and infrastructure as code
- Production experience deploying AI agents, LLM infrastructure, or MCP servers
- Experience deploying, managing, and scaling AI-driven solutions, vector databases, proxy gateways, or MCP servers
- Strong CI/CD and GitOps experience for Kubernetes-hosted services
- Terraform or equivalent infrastructure-as-code experience
- Practical cloud operations experience, preferably Azure
- Python scripting for pipeline tooling, release checks, operational automation, and AI evaluation SDK integration
- Experience with secrets management, container security, workload isolation, and dynamic agent sandboxing
- Experience with monitoring, alerting, telemetry, and LLM or agent tracing
- Experience enforcing automated tests and LLM evaluation results in CI
- Experience with canary, blue-green, or progressive delivery strategies
- Experience with LLM or agent evaluation frameworks such as DeepEval, Ragas, or LangSmith
- Experience with software supply-chain controls, artifact signing, and dependency or security scanning
What We Do
Convo provides an enterprise social collaboration platform that enables easy, secure conversations between desk / non-desk workers to accelerate company productivity and engagement. Unlike existing email-focused or chat-centric collaboration platforms, only Convo combines the ease of social networks with rich collaboration capabilities to simplify and optimize work interactions for all employees -- no email required.
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