AI Engineer

Role overview

Responsibilities

• check_circle As the AI Engineer at the Bank within the Data Analytics & Artificial Intelligence division, you will design, develop, and deploy production-grade AI solutions using Microsoft Azure AI Services and Python.
• check_circle Your focus will be on advancing the bank's capabilities in Generative AI, Agentic AI, intelligent automation, knowledge search, and content summarization by building scalable, secure, reliable, and responsible AI systems that enhance operational efficiency, customer engagement, and decision intelligence across the Group.
• check_circle The AI Engineer will work closely with Data Scientists, Platform Engineers, DevOps/MLOps Engineers, Business Analysts, and product teams to deliver end-to-end AI solutions.
• check_circle The role is critical for driving the organization's digital transformation agenda, empowering business users with advanced AI-powered tools, and improving operational efficiency through intelligent automation using GERNAS OS platform and Agent Development Kit (ADK).

Basic qualifications

• Candidates should have 1-2 years of experience, but a relevant certification and foundational knowledge of desktop/laptop troubleshooting are essential

Preferred qualifications

• Knowledge of Bloomberg and in-depth of audio-visual devices.
• Setting up a meeting room from Scratch
• minimum 3-4 yrs of working experience mandatory
• Demonstrated working experience with Microsoft Azure AI Services including Azure OpenAI, Azure Machine Learning, Azure Cognitive Services, Azure AI Search, Azure Functions, and Azure Databricks.
• Strong proficiency in Python programming, including experience with REST APIs, SDKs, asynchronous processing, data manipulation, backend development, and AI application frameworks (LangChain, LlamaIndex, Semantic Kernel, LangGraph, FastAPI).
• Deep understanding of machine learning, statistical modeling, NLP, generative AI principles, LLM application development, prompt engineering, RAG architecture, embeddings, vector databases, semantic search, and model evaluation techniques.
• Advanced proficiency in ML libraries such as PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, and NLP libraries (spaCy, NLTK).
• Experience with vector databases including FAISS, Azure AI Search, ChromaDB, and Pinecone.
• Hands-on experience with DevOps/MLOps practices and tools such as Git, Docker, Kubernetes, CI/CD pipelines, MLflow, Terraform, Azure Monitor, and Application Insights.
• Understanding of cloud security, identity and access management, data privacy, encryption, logging, monitoring, and secure API integration patterns.
• Awareness of ethical considerations and responsible AI practices, including fairness, accountability, transparency, bias detection, hallucination mitigation, and compliance in AI systems.