We are looking for an AI Plarform Engineer to join our team.

Responsibilities

1 — AI Platform & LLM Integration Design and operate the enterprise AI platform layer, integrating and managing multiple LLM providers in a production environment:
- Multi-provider LLM integration across Azure AI Foundry, Azure OpenAI, Claude (AWS Bedrock), and OpenAI APIs — with routing, fallback, and cost controls
- Reusable AI services and APIs consumed by agents, copilots, and business applications
- Context management, memory architectures, structured outputs, and function calling
- Inference performance optimisation, cost attribution, and usage analytics
- AI environment management — prompt versioning, model versioning, deployment pipelines
2 — Agent & Agentic Workflow Development Design and deploy autonomous agents and multi-agent systems that execute real business workflows:
- Autonomous and planning agents capable of multi-step reasoning over enterprise knowledge
- Multi-agent orchestration using Model Context Protocol (MCP) and agent collaboration frameworks
- Tool-using agents with function calling, plugin architecture, and enterprise system integrations
- AI copilots built on Microsoft Copilot Studio for business users
- Workflow automation connecting AI agents to enterprise platforms (SharePoint, Salesforce, M365, etc)
3 — RAG & Knowledge Retrieval Build enterprise-grade retrieval capabilities on top of the Corporate Brain data platform:
- Hybrid search (semantic + keyword) with vector indexing and Azure AI Search integration
- Context retrieval, citation frameworks, and prompt orchestration for grounded, accurate outputs
- Chunking strategies, embedding pipeline consumption, and knowledge grounding
- RAG evaluation — precision, recall, hallucination detection, and context relevance testing.
4 — Evaluation, Safety & AI Governance Own the quality and safety bar for all AI systems in production — this is a first-class engineering responsibility, not an afterthought:
- Systematic evaluation frameworks covering accuracy, hallucination detection, and response quality
- Prompt regression testing, agent benchmarking, and safety testing across model updates
- Guardrails design and implementation — input/output filtering, content policy enforcement
- Production monitoring: latency, cost per query, error rates, and agent task completion rates
- AI lifecycle governance — structured approval gates before production deployment.

Required Technical Skills

LLM APIs & prompt engineering
Retrieval Augmented Generation (RAG)
AI agents & agentic workflows
Function calling & structured outputs
Vector databases & semantic search
Python
REST APIs, Docker, Git, CI/CD
Azure & cloud-native development
Multi-provider LLM integration
AI evaluation & safety frameworks

Experience

8+ years in software engineering, with at least 3 years building and shipping production AI applications
Proven experience deploying enterprise LLM solutions used by real end-users at scale
Experience designing multi-agent systems and agentic orchestration architectures
Experience integrating AI into business workflows — not just standalone demos.

Nice to Have

LangGraph
Semantic Kernel
LangChain or LlamaIndex
AutoGen / CrewAI
Observability platforms (Datadog, Grafana)
AI governance frameworks
Azure AI Search (advanced)
Fine-tuning or RLHF experience

What can Syone offer me

Integration in an organization with profound and sustained growth and involvement in pioneering projects with innovative technological solutions; Strong IT training plans; Professional evolution with intervention in ambitious technological projects, both national and internationally.

AI Platform Engineer - Syone Lisbon (Hybrid), Porto (Hybrid)

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