{"id":1175492,"url":"https://alion.io/job/saglobal-senior-ai-platform-engineer","title":"Senior AI Platform Engineer","company":{"id":691917,"name":"Saglobal","domain":"saglobal.com","url":"https://alion.io/company/saglobal-2","size_band":"201-500","is_staffing_agency":false,"is_intermediary":false,"listed_via":null,"ats_vendor":"Breezy","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-24T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Belgrade, Serbia"],"countries":["RS"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":50000,"max_usd":124000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1271},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"DSPy","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Neo4j","optional":false},{"name":"Ollama","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"pgvector","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Tool Use","optional":false},{"name":"vLLM","optional":false},{"name":"Databricks","optional":true},{"name":"LangChain","optional":true},{"name":"LLM Evaluation","optional":true}],"status":"live","first_seen_at":"2026-09-24T11:06:35Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-09-24T20:11:22Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Job Title: Senior AI Platform Engineer\nLocation: Belgrade Serbia or Lisboa/Porto/Braga, Portugal\nWorking Model: Hybrid (flexible, depending on candidate location)\nReports to: sa.global Labs Leaderhip\nDepartment: sa.global Labs - Core Team (team working on AI development within sa.global)\nSeniority Level: Senior\nempower AI Platform - Senior AI Platform Engineer\nAbout the Role\nAnyone 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.\nIt'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.\nAbout empower AI Platform\nempower 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:\n Corporate Knowledge (Ontology): the structured semantic model of a client's domain that gives agents grounded, domain-correct context.\nData Hub: the layer that unifies, cleans, and serves enterprise data into the platform.\nMulti-Agent Skills: the orchestration layer where reasoning, tool use, and multi-step automation happens.\nPlatform Stack\nempower is built on a specific, opinionated stack. Prior hands-on experience with these, not just the general category, is what we're screening for:\nPrefect: orchestration and scheduling data and processing pipelines.\nPostgreSQL with pgvector and pg_search extensions: our production vector store and hybrid full text/semantic search layer.\nNeo4j: the graph database underlying the Business Knowledge Graph.\nMLflow: experiment tracking and lifecycle management for models and prompt/agent evaluation runs.\nLlamaIndex: retrieval and RAG pipeline framework.\nDSPy: declarative prompt programming and optimization.\nLangGraph: agent orchestration and control flow.\nThis 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.\nCore responsibilities\nDesign, build, and operate production-grade REST APIs and backend services that power empower's Data Hub and Agent Workflows.\nIntegrate open-source and commercial AI/LLM tools into backend systems: RAG pipelines, embeddings, vector search, model routing across providers.\nBuild and maintain the data pipelines and integrations that feed the Business Knowledge Graph.\nOwn reliability, performance, and security of the services you build, monitoring, cost, and scaling included.\nCollaborate with AI Product Engineers to expose the right platform primitives, including tool endpoints, retrieval APIs, and data contracts, for agent workflows to build on.\nRequired technical experience\n5+ years of backend software development;Python as the primary language for AI-adjacent work.\nProven track record of designing and shippingproduction APIs and distributed services, not just prototypes.\nHands-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.\n Working knowledge of retrieval architectures built on PostgreSQL with pgvector and pg_search (our production stack), chunking, hybrid vector/full-text search.\nFamiliarity with MCP (Model Context Protocol) for exposing tools and data to AI agents.\nComfort with cloud-native deployment (Azure preferred, given our Azure DevOps stack; AWS/GCP experience transfers fine), containerization, and CI/CD.\n 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.\nNice to have\nFamiliarity withNeo4j or other graph databases.\nExposure to MLFlow or another experiment-tracking/model-lifecycle tool.\n Hands-on experience with Prefect (or a comparable orchestrator like Airflow) for building and scheduling data and processing pipelines.\n Working knowledge of LlamaIndex (or a comparable RAG framework) for building retrieval pipelines over enterprise data.\nExperience with knowledge-graph orontology-backed systems.\nPrior exposure to services-centric (legal, Architecture/Engineering/Construction or ERP domains).\nExperience with Azure AI, Databricks, or similar enterprise data platforms.\nSoft Skills & Working Competencies\nTechnical 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.\nAgency\nThe 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.\nSystemic Thinking\nThe 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.\nStructured Communication for AI-Directed Work\nThe 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.\nOwnership & Quality Discipline\nTreating 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.\nComfort with Ambiguity\nThe 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.\nFor more information, visit www.saglobal.com.","description_format":"text","description_chars":7459,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Serbia","iso":"RS","kind":"country"},{"name":"Portugal","iso":"PT","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-09-24T11:36:23Z"}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":46,"reasons":["conf:0","win:early"],"computed_at":"2026-09-24T21:07:11Z"},"pay":null,"html_url":"https://alion.io/job/saglobal-senior-ai-platform-engineer","json_url":"https://alion.io/job/saglobal-senior-ai-platform-engineer.json","meta":{"generated_at":"2026-09-24T21:07:11Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}