{"id":3862536,"url":"https://alion.io/job/convo-senior-ai-devops-platform-engineer-ai-agents-mcp","title":"Senior AI DevOps / Platform Engineer (AI Agents & MCP)","company":{"id":4421849,"name":"Convo","domain":"convo.com","url":"https://alion.io/company/convo-4","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"JobScore","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Islamabad, Pakistan"],"countries":["PK"],"hiring_countries":["PK"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":9,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Arize Phoenix","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Datadog","optional":false},{"name":"GitOps","optional":false},{"name":"Kubernetes","optional":false},{"name":"Langfuse","optional":false},{"name":"Least Privilege","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Terraform","optional":false},{"name":"DeepEval","optional":true},{"name":"LangSmith","optional":true},{"name":"Progressive Delivery","optional":true},{"name":"Ragas","optional":true}],"status":"live","first_seen_at":"2026-11-06T06:21:16Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-09T21:38:22Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Technical mission\nBuild, 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.\nKey responsibilities\nDeploy, host, scale, and operationalize AI Agent frameworks, vector stores, and Model Context Protocol (MCP) servers across Kubernetes clusters.\n\nDesign CI/CD pipelines for Python services, agent definitions, prompts, tools, MCP servers, and platform configuration.\n\nImplement GitOps-based Kubernetes deployments with promotion, rollback and environment-specific policy controls.\n\nIntegrate automated unit, API, security and LLM-evaluation checks (e.g., prompt stability, tool-use accuracy, output safety, latency/cost regression) into release gates.\n\nImplement secrets handling, dynamic sandbox isolation for agent execution, and least-privilege deployment identities.\n\nInstrument pipelines and deployed services for agent-level observability (token usage, tool invocation tracking, LLM latency, cost monitoring, distributed tracing), health monitoring, and incident diagnosis.\n\nMaintain reusable deployment templates for services, agents, and MCP tools, and coach engineering teams on release standards.\n\nRequired technical capabilities\nMinimum 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.\n\nHands-on experience deploying, managing, and scaling AI-driven solutions, vector databases, proxy gateways, or MCP servers in production.\n\nStrong CI/CD and GitOps experience for Kubernetes-hosted services.\n\nTerraform or equivalent infrastructure-as-code and practical cloud operations experience, preferably Azure.\n\nPython scripting for pipeline tooling, release checks, operational automation, and integrating AI evaluation SDKs.\n\nSecrets management, container security, workload-isolation practices, and dynamic agent sandboxing.\n\nMonitoring, alerting, and telemetry for both delivery pipelines and runtime services (including LLM/Agent tracing tools like LangFuse, Phoenix/Arize, OpenTelemetry, Datadog, or Prometheus).\n\nExperience making automated test and LLM evaluation results enforceable in CI.\n\nPreferred experience\nRelease strategies such as canary, blue/green and progressive delivery for agent prompt changes and LLM versioning.\n\nLLM or agent evaluation frameworks (e.g., DeepEval, Ragas, LangSmith) and benchmark execution in CI.\n\nSoftware supply-chain controls, artifact signing and dependency/security scanning.\n\nExpected deliverables / acceptance evidence\nReusable CI/CD templates for services, agents, MCP tools, and framework releases.\n\nAutomated quality, security and LLM-evaluation gates.\n\nGitOps deployment definitions with tested rollback paths.\n\nRelease observability, AI tracing, audit trail and operational documentation.\n\nPrimary interfaces\nWorks with Infrastructure, Agent Runtime, QA Automation, AI Test & Dataset Engineering and service owners across all pods.\nWhat We Have For You\nGreat 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!\n\nConvo endorses a culture of diversity in all aspects and aims to build a diverse team of amazing individuals!","description_format":"text","description_chars":3582,"description_truncated":false,"requirements":{"experience_years_min":9,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Pakistan","iso":"PK","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial 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