We're supporting a fast-growing Agentic AI product company, headquartered in Dubai, UAE, in building its core engineering team.
This company isn't building another AI tool or LLM wrapper.
They're building autonomous AI agents that perceive, plan, decide, and act inside real enterprise environments — across CRMs, databases, compliance systems, and operational workflows.
As an early AI Engineer, you'll architect intelligent agent systems that operate in production enterprises — not just prototypes or demos.
If you want to build foundational AI systems that redefine how software behaves, this role is for you.
Why This Role
- Build autonomous enterprise AI agents — not chatbots
- Shape real-world agent architectures and deployment patterns
- Work across multi-agent, RAG, and orchestration systems
- Influence how enterprises adopt and trust AI
- Use AI tools to amplify your own engineering productivity
What You'll Do
- Design and deploy advanced AI agents using orchestration layers (n8n, Temporal, agentic stacks)
- Build context-aware workflows integrated with enterprise systems
- Prototype rapidly and ship production-grade agent flows
- Collaborate with product/design to identify high-impact AI use cases
- Push the boundaries of RAG, embeddings, and vector search
- Engineer integrations across LLMs (OpenAI, Claude, etc.) and enterprise APIs
- Develop multi-agent orchestration (planner-executor, memory, tool-use, personas)
- Own the AI pipeline end-to-end: prompts → context → evaluation → deployment
- Define reliability, latency, and safety standards for AI outputs
What you Bring
- Bachelor's in Computer Science or equivalent experience
- 8+ years in applied ML / NLP / AI systems engineering
- Deep experience with LLMs (GPT-4, Claude, or similar)
- Hands-on RAG pipelines with embeddings & vector databases
- Experience with multi-agent or orchestration architectures
- Strong Python and ML frameworks (PyTorch / TensorFlow)
- Familiarity with LangChain, LangGraph, Temporal, n8n, or similar
- Solid enterprise engineering practices (APIs, CI/CD, monitoring)
- Excellent collaboration in fast-moving startup environments
Nice To Have
- Experience deploying AI agents in enterprise workflows
- Evaluation frameworks for LLM/agent systems
- Multi-agent simulation or planning architectures
- Production AI safety / guardrails experience
