Ai Platform Engineer

We are looking for an AI Plarform Engineer to join our team.
Responsibilities
1. 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. 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. 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. 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.