Senior AI Platform Engineer

Job description

Job Title: Senior AI Platform Engineer Location: Belgrade Serbia or Lisboa/Porto/Braga, Portugal Working Model: Hybrid (flexible, depending on candidate location) Reports to: Labs Leaderhip Department: Labs – Core Team (team working on AI development within ) Seniority Level: Senior empower AI Platform - Senior AI Platform Engineer About the Role Anyone can call an LLM API. We're building the harness around it: Labs is expanding the core team behind empower — our industry-specific agentic AI platform that fuses a domain knowledge graph, a unified data hub, and multi-agent orchestration to automate real enterprise decisions, not demos. We're looking for a Senior AI Platform Engineer to build and operate the scalable foundation underneath it: the APIs, distributed services, data pipelines, and retrieval infrastructure that connect enterprise data, knowledge, and AI capability across the platform. It's a deliberately opinionated stack — Neo4j for the Business Knowledge Graph, PostgreSQL with pgvector/pg_search for hybrid retrieval, Prefect for pipelines, LangGraph and DSPy powering the agent layer above you — and this is a hands-on senior role: you're building the reliable, secure, production-grade systems that carry real AI workloads for clients across multiple industries, not prototyping in a sandbox. About empower AI Platform empower is a proactive agentic intelligence layer. It's built around 3 pillars, and this role touches all of them, with a focus on the backend foundation: Corporate Knowledge (Ontology): the structured semantic model of a client's domain that gives agents grounded, domain-correct context. Data Hub: the layer that unifies, cleans, and serves enterprise data into the platform. Multi-Agent Skills: the orchestration layer where reasoning, tool use, and multi-step automation happens. Platform Stack empower is built on a specific, opinionated stack. Prior hands-on experience with these, not just the general category, is what we're screening for: Prefect: orchestration and scheduling data and processing pipelines. PostgreSQL with pgvector and pg_search extensions : our production vector store and hybrid full text/semantic search layer. Neo4j: the graph database underlying the Business Knowledge Graph. MLflow: experiment tracking and lifecycle management for models and prompt/agent evaluation runs. LlamaIndex: retrieval and RAG pipeline framework. DSPy: declarative prompt programming and optimization. LangGraph: agent orchestration and control flow. This role owns the platform foundation – the APIs, data pipelines, services, and infrastructure that everything else runs on – and integrates open-source and commercial AI capabilities into that foundation cleanly and reliably. Core responsibilities Design, build, and operate production-grade REST APIs and backend services that power empower's Data Hub and Agent Workflows. Integrate open-source and commercial AI/LLM tools into backend systems: RAG pipelines, embeddings, vector search, model routing across providers. Build and maintain the data pipelines and integrations that feed the Business Knowledge Graph. Own reliability, performance, and security of the services you build, monitoring, cost, and scaling included. Collaborate with AI Product Engineers to expose the right platform primitives, including tool endpoints, retrieval APIs, and data contracts, for agent workflows to build on. Required technical experience 5+ years of backend software development; Python as the primary language for AI-adjacent work. Proven track record of designing and shipping production APIs and distributed services , not just prototypes. Hands-on experience integrating LLM/AI APIs (Anthropic, OpenAI, or open-weight models via vLLM/Ollama) into real applications. This is application integration, not model research. Working knowledge of retrieval architectures built on PostgreSQL with pgvector and pg_search (our production stack), chunking, hybrid vector/full-text search. Familiarity with MCP (Model Context Protocol) for exposing tools and data to AI agents. Comfort with cloud-native deployment (Azure preferred, given our Azure DevOps stack; AWS/GCP experience transfers fine), containerization, and CI/CD. Daily, hands-on use of Claude Code or a comparable AI coding agent (Cursor, Copilot, Codex) as a working method, scaffolding, refactoring, test generation, and codebase navigation, not just occasional use. Nice to have Familiarity with Neo4j or other graph databases. Exposure to MLFlow or another experiment-tracking/model-lifecycle tool. Hands-on experience with Prefect (or a comparable orchestrator like Airflow) for building and scheduling data and processing pipelines. Working knowledge of LlamaIndex (or a comparable RAG framework) for building retrieval pipelines over enterprise data. Experience with knowledge-graph or ontology-backed systems. Prior exposure to services-centric (legal, Architecture/Engineering/Construction or ERP domains). Experience with Azure AI, Databricks, or similar enterprise data platforms. Soft Skills & Working Competencies Technical range alone won't succeed on this team. empower is built by a small, AI-native team working across genuinely different domains, often ahead of settled best practice, and increasingly through AI coding agents rather than only with them. The competencies below are assessed in interviews, not treated as filler. Each comes with a working definition so there's no ambiguity about what's expected. Agency The capacity to identify what needs to happen and act on it without waiting to be told, spotting a gap, defining the goal, and mobilizing the tools (including AI agents) and people needed to close it, while owning the outcome. This is distinct from raw autonomy: autonomy is being able to work unsupervised; agency adds the initiative to decide what is worth doing next. In this role, it looks like: flagging a platform risk before it's assigned to you, proposing the fix, and driving it to done, including deciding when an AI coding agent can execute the plan and when it can't. Systemic Thinking The ability to reason about a component in terms of its effect on the whole system, not just its local correctness, understanding how a change in one part of empower (a retrieval change, an agent policy, an ontology edit) propagates through data flows, other agents, and end-client outcomes. It includes seeing feedback loops and second-order effects, not just the immediate diff. Structured Communication for AI-Directed Work The ability to write clear, well-scoped instructions, specs, and delegation, to teammates and AI coding agents alike, precisely enough that the recipient (human or model) doesn't have to guess. Includes decomposing large problems into well-bounded tasks and knowing what to delegate versus what to do by hand. Ownership & Quality Discipline Treating code, tests, and review as your responsibility regardless of whether a human or an AI agent wrote the first draft, building the feedback loops (types, tests, evaluation harnesses, review) that keep quality high when a growing share of code is AI-generated. Comfort with Ambiguity The ability to make sound technical progress in areas where the tooling, frameworks, or client requirements are still settling, common in agentic AI and in translating enterprise ontology work across service-centric domains. For more information, visit .