{"id":4195168,"url":"https://alion.io/job/alliance-for-recruitment-senior-agentic-platform-engineer","title":"Senior Agentic Platform Engineer","company":{"id":4425623,"name":"Alliance for Recruitment","domain":"afr.lt","url":"https://alion.io/company/alliance-for-recruitment","size_band":"11-50","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"DevOps","role_family":"DevOps","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":"inferred","locations":["Vilnius, Lithuania"],"countries":["LT"],"hiring_countries":["LT"],"hiring_countries_total":1,"salary":{"min":6000,"max":7500,"currency":"EUR","period":"month","gross":true,"usd_annual":99504},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"ArgoCD","optional":false},{"name":"Claude","optional":false},{"name":"Claude Code","optional":false},{"name":"Data Vault","optional":false},{"name":"Function Calling","optional":false},{"name":"GitOps","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Scrum","optional":false},{"name":"Structured Outputs","optional":false},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"MLFlow","optional":true},{"name":"Neo4j","optional":true},{"name":"OpenAI Agents SDK","optional":true}],"status":"live","first_seen_at":"2026-11-12T14:17:44Z","employer_posted_date":null,"last_verified_at":"2026-11-12T14:17:44Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Job description\nAbout the role\n\nYou’ll join VaultSpeed’s small Agentic team as a hands-on senior engineer, building AI agents that customers rely on in production. The platform runs on Omnigent, an open-source agent platform that VaultSpeed extends for enterprise use, with a graph-based context store and Data Vault expertise packaged into the agents as skills.\n\nThe work spans the whole stack: the agents and their skills, the platform services they run on, the Kubernetes infrastructure behind them and the interface people use. You’ll own areas of the system from technical design to release, and your decisions will shape how a new product grows.\n\nWhat you’ll do\n\nBuild and improve the agents that ingest sources and generate data models and code, and the skills that carry data modelling knowledge, deciding what belongs in a prompt, a skill, a tool or plain deterministic code\n\nBuild evaluations and regression suites, so changes to prompts, models and tools are judged on evidence\n\nExtend the platform layer on top of Omnigent: multi-tenancy and tenant isolation, authentication, the MCP tool suite, the context store, the agent marketplace and the LLM gateway\n\nKeep the Omnigent fork in sync with upstream, resolve conflicts with customisations and contribute fixes back to the open-source project\n\nWork on the Kubernetes sandbox infrastructure where agents run (templates, warm pools, persistence, suspend and resume, observability, cost) and ship changes through GitOps with Argo CD\n\nInvestigate agent failures through logs and model traces, and fix root causes rather than symptoms\n\nWrite technical proposals, review designs and pull requests, and support releases\n\nRequirements5+ years of professional software engineering, including owning the technical design of significant components through to production\nHands-on experience shipping LLM-based features to real users (beyond prototypes): tool calling, structured outputs, context management\nStrong back-end fundamentals: services and APIs, authentication, data modelling, databases\nDocker and hands-on experience deploying and debugging services on Kubernetes through a GitOps workflow\nComfortable working in a large codebase you don’t own and resolving non-trivial merge conflicts\nStrong analytical thinking: you get from a vague symptom to a root cause, also in non-deterministic systems\nDaily use of AI coding tools such as Claude Code or Cursor\nExperience working in a Scrum team across a distributed setup\nFluent English\nNice to have\nMCP, the Claude Agent SDK, LangGraph or similar agent frameworks\nLLM evaluation and observability tools such as MLflow or Langfuse\nGraph databases (Neo4j)\nMulti-tenant SaaS and identity: OIDC, SSO, WorkOS or similar\nKubernetes agent-sandbox or other sandboxed execution environments\nData engineering or data warehousing\nMaintaining a fork of, or contributing to, an active open-source project\nComfortable making minor front-end changes\nCompany offersBuilding an AI product in a well-defined domain, where agent output can be checked against a real model\nJoining a new product early, in a small team where your design decisions shape the platform\nRemote work in a team organised around trust and outcomes\nPrivate health insurance\nCompany 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