AI Engineer L2 · 4+ years (Python ML / GenAI)

Role Summary

We are looking for an AI Engineer to design, prototype, and operationalise AI/ML solutions — including classical

ML models and modern Generative AI patterns (RAG, agents, fine-tuning). The role bridges research,

engineering, and production deployment.

Key Responsibilities

- Build end-to-end ML pipelines: feature engineering, training, evaluation, deployment, monitoring.

- Design and implement GenAI applications (RAG, agentic workflows, structured-output prompting).

- Operationalise models (containers, serving frameworks, A/B testing, drift monitoring).

- Evaluate and benchmark LLMs, embeddings, and vector stores.

- Apply responsible-AI practices (eval frameworks, content safety, PII handling).

- Collaborate with data engineers, product, and security teams.

- Document experiments and decisions with reproducibility in mind.

Required Qualifications

- Bachelor's/Master's in CS, Data Science, ML, or equivalent.

- 4+ years applied ML engineering; recent hands-on with GenAI / LLMs.

- Strong Python; deep familiarity with PyTorch and/or TensorFlow.

- Hands-on with at least one LLM stack: Azure OpenAI, OpenAI, Anthropic, Bedrock, Vertex AI.

- Experience with vector databases (pgvector, Pinecone, Weaviate, FAISS, Chroma).

- Comfortable with MLOps tooling (MLflow, Weights & Biases, SageMaker, Azure ML, or similar).

- Professional English — mandatory.

Preferred / Nice to Have

- Working knowledge of Arabic is a plus, including Arabic NLP awareness.

- Experience with Arabic language models / multilingual embeddings.

- Cloud ML certifications (Azure AI Engineer, AWS ML Specialty, GCP ML Engineer).

- Familiarity with agent frameworks (LangChain, LlamaIndex, Semantic Kernel).