Senior Ai Product Engineer, Agentic Systems

Job Title: Senior AI Product Engineer
Location: Belgrade, Novi Sad - Serbia or Lisboa/Porto/Braga, Portugal
Working Model: Hybrid (flexible, depending on candidate location)
Reports to: sa.global Labs Leaderhip
Department: sa.global Labs – Core Team (team working on AI development within sa.global)
Seniority Level: Senior
empower AI Platform - Senior AI Product Engineer, Agentic Systems
About the Role
If you've ever shipped an agent that worked great in the demo and fell apart in production, this is the role that fixes that - for real clients, at real scale.
sa.global Labs is looking for a Senior AI Product Engineer to own the reasoning core of empower, our industry-specific agentic AI platform: the agent orchestration, prompt architecture, and evaluation systems that turn agentic behavior into automation clients can actually trust. You'll build on LangGraph, DSPy, and MLflow, reasoning over a Neo4j-backed knowledge graph and hybrid retrieval layer, with real latitude to decide when agentic complexity is warranted and when it isn't.
If you're a Senior AI Platform Engineer whose interest keeps pulling toward the agent layer, this is where that curiosity turns into ownership.
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 applied AI/agent layer:
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:
LangGraph:
agent orchestration and control flow.
DSPy:
declarative prompt programming and optimization.
LlamaIndex:
retrieval and RAG pipeline framework.
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.
Prefect:
orchestration and scheduling of data and processing pipelines.
This role builds the reasoning and agentic core of empower, the backend of the platform that decides, plans, and acts, and that has to be evaluated and trusted rather than just shipped and hoped for.
Core responsibilities
Design and implement multi-agent workflows and orchestration graphs using frameworks such as
LangGraph.
Apply
DSPy
or comparable declarative prompt-programming approaches so that prompting is systematic, testable, and optimizable, not hand-tuned strings that rot with every model update.
Build and maintain evaluation harnesses — tracked in MLflow —
that measure accuracy, faithfulness/hallucination rate, latency, and instruction-following across model and prompt versions.
Architect
agent memory,
tool-calling, guardrails, and human-in-the-loop controls for production agent workflows serving real client decisions.
Prototype retrieval architectures on top of the Neo4j-backed Business Knowledge Graph (GraphRAG, hybrid search with LlamaIndex and pgvector/pg_search) in partnership with Level I engineers.
Push
technical judgment calls
on when an agentic pattern is warranted versus when a simpler deterministic path is the right call, this role is expected to have and defend that opinion.
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
for scaffolding, refactoring, test generation, and codebase navigation, not just occasional use — using it to prototype and stress-test agent architectures, generate evaluation scaffolding, and