AI/ML Engineer – Generative AI & MLOps (Dubai)

About the role

We are seeking an experienced AI/ML Engineer to design, build, and operationalize AI and machine learning systems that deliver measurable business value for our financial services division. The role moves models from experimentation to robust, scalable production services.

Key responsibilities

• Develop and integrate generative AI and LLM solutions, including prompt engineering, RAG architectures, embeddings, and model fine‑tuning.
• Design and implement agentic AI workflows with tool/function calling and human‑in‑the‑loop controls.
• Build, train, and deploy ML/DL models for prediction, classification, NLP, recommendation, and computer vision use cases.
• Create end‑to‑end ML pipelines covering data ingestion, feature engineering, training, evaluation, deployment, and monitoring.
• Operationalize models using MLOps practices: CI/CD, versioning, automated retraining, A/B testing, and drift monitoring.
• Collaborate with data engineers to ensure reliable data flows and feature stores.
• Optimize models and inference infrastructure for latency, throughput, scalability, and cost.
• Implement observability, logging, and alerting; address data drift and model degradation.
• Embed responsible AI principles—fairness, explainability, security, privacy, and compliance—throughout the lifecycle.

Required profile

• 3–7+ years of hands‑on experience building and deploying AI/ML systems in production.
• Strong Python programming skills with solid software‑engineering fundamentals.
• Practical experience with LLMs, generative AI, prompt design, RAG, embeddings, and vector databases.
• Proficiency with ML frameworks such as scikit‑learn, PyTorch, and/or TensorFlow.
• Experience deploying models as services (REST/gRPC APIs, containerization, batch and real‑time inference).
• Strong data skills: SQL, pandas/Spark, handling large real‑world datasets.
• Experience with at least one major cloud platform (Azure, AWS, or GCP) and its ML services.
• Familiarity with MLOps tooling and model monitoring/lifecycle management.

Required skills

• Python
• SQL
• scikit‑learn
• PyTorch
• TensorFlow
• Azure ML / AWS SageMaker / Google Vertex AI
• Spark, Databricks, Pandas, Delta Lake
• Docker, Kubernetes
• MLflow, Git, CI/CD pipelines
• LangChain / LangGraph / LlamaIndex
• FAISS, Pinecone, Weaviate, pgvector
• OpenAI, Anthropic, Azure OpenAI, Llama, Mistral