Senior ai platform engineer

Empower AI Platform
- Senior AI Platform Engineer

About the Role
Anyone can call an LLM API. We're building the harness around it: sa.global 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, Postgre SQL with pgvector/pg_search for hybrid retrieval, Prefect for pipelines, Lang Graph 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. Postgre SQL 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. Llama Index: retrieval and RAG pipeline framework. DSPy: declarative prompt programming and optimization. Lang Graph: 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, Open AI, or open-weight models via v LLM/Ollama) into real applications. This is application integration, not model research. Working knowledge of retrieval architectures built on Postgre SQL 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 Dev Ops 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 Llama Index (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