AI Engineer - Agentic Systems & Applied AI

Eazli | Future Living, headquarter in Jeddah, Saudi Arabia is redefining how the Middle East lives, designs, and builds, an AI-native marketplace connecting homeowners with products and home services across Saudi Arabia, the UAE, and Egypt. Built on a multilanguage models and a fleet of specialized AI agents, we’re not a startup chasing trends; we’re architecting the infrastructure layer of the MENA home economy, with ambitions stretching into Europe and beyond. If you want to work on real AI that converses, reasons, and transacts at scale, and you want your work to matter to millions of people making their most personal spaces their own - Eazli is where that happens.

Eazli is building a multi-agent AI platform for commerce workflows across customers, vendors, products, inventory, orders, profile management, market research, interior redesign, and knowledge search. We are looking for an AI Engineer who can design, build, test, and operate production-grade AI agents that work with real business systems, not just demos.

You're hands-on AI Engineer with strong experience in building intelligent, agent-driven systems. The ideal candidate thrives in fast-paced environments, enjoys solving ambiguous problems, and is passionate about pushing the boundaries of applied AI.

Key Responsibilities:

1. Build and maintain AI agents using Python, Agno, OpenAI/Gemini/Ollama integrations, and custom tool executors.

2. Design agent workflows for customer and vendor use cases such as product search, cart/order management, inventory updates, profile management, service search, market research, and redesigning.

3. Implement reliable tool-calling agents that interact with ERP APIs, S3 artifacts, vector databases, and external services.

4. Improve Eazli’s multi-agent architecture using A2A communication, AG-UI streaming, Redis-based registry/check-ins, and team-based delegation.

5. Create and maintain YAML-driven agent configurations, prompts, tool schemas, routing behavior, and workflow judges.

6. Build RAG/knowledge features using embeddings, Milvus/vector search, and domain-specific retrieval logic.

7. Write agentic tests for persona compliance, tool usage, guardrails, context handling, and expected outputs.

8. Work with FastAPI, Pydantic, Redis/Valkey, Postgres, S3/MinIO, Docker, and Helm/Kubernetes-oriented deployment patterns.

9. Add observability and debugging support using OpenTelemetry, Phoenix/Arize, Sentry, logs, and structured traces.

10. Collaborate with backend teams to integrate AI services (FastAPI experience is a plus)

11. Continuously experiment, research, evaluate, and improve AI models and system performance

Requirements:

1. 4+ years of experience in AI/ML, including Deep Learning and Reinforcement Learning.

1. Strong Python engineering experience, preferably Python 3.12+.

2. Strong hands-on experience with agentic frameworks (LangChain, LangGraph, CrewAI).

1. Experience building LLM applications with tool/function calling and structured outputs.

2. Hands-on experience with OpenAI APIs or similar model providers such as Gemini, Anthropic, or local models.

3. Practical expereince of agent frameworks or orchestration patterns, such as Agno, LangGraph, CrewAI, AutoGen, or similar.

4. Solid understanding of vector databases(Milvus, Pinecone, Qdrant), graph design, and RAG architectures.

8. Experience designing scalable, production-grade AI systems.

1. Ability to design robust prompts, agent instructions, guardrails, and evaluation datasets.

2. Experience with A2A protocol, AG-UI protocol, MCP, or multi-agent communication systems.

3. Experience integrating AI systems with business APIs and databases.

4. Strong testing mindset, including unit tests, integration tests, and LLM/agent behavior evaluations.

5. Familiarity with Redis, Postgres, object storage, Docker, and cloud-native deployment basics.

6. Good judgment around security, gaurdrails, privacy, observability, and production reliability.

7. Ability to work in ambiguous, fast-changing environments with minimal supervision.

8. Experience with Kubernetes, Helm, CI/CD, and production observability.

9. Experience with Temporal workflows.

10. Strong problem-solving mindset with a bias for action.

What We’re Looking For:

1. Highly passionate about AI, innovation, research and continuous learning

2. Comfortable challenging existing approaches and exploring new ideas

3. Startup mindset: ownership, speed, and adaptability

4. Curiosity-driven, with a strong inclination toward research and experimentation