AI Engineer

Roles Responsibilities As the AI Engineer at the Bank within the Data Analytics Artificial Intelligence division you will design develop and deploy productiongrade AI solutions using Microsoft Azure AI Services and Python 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 The AI Engineer will work closely with Data Scientists Platform Engineers DevOps MLOps Engineers Business Analysts and product teams to deliver endtoend AI solutions The role is critical for driving the organization s digital transformation agenda empowering business users with advanced AIpowered tools and improving operational efficiency through intelligent automation using GERNAS OS platform and Agent Development Kit ADK Role Overview The position is a Level 1 technical support role focused on laptop configuration delivery and postimplementation support for applications like Outlook and Teams Desired Candidate Profile Experience Skills Candidates should have 12 years of experience but a relevant certification and foundational knowledge of desktop laptop troubleshooting are essential Knowledge of Bloomberg and indepth of audiovisual devices Setting up a meeting room from Scratch TECHNICAL SKILLS minimum 34 yrs of working experience mandatory Azure AI Services 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 Python Programming 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 LLM Generative AI 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 ML Libraries Frameworks Advanced proficiency in ML libraries such as PyTorch TensorFlow Hugging Face Transformers scikitlearn and NLP libraries spaCy NLTK Vector Databases Experience with vector databases including FAISS Azure AI Search ChromaDB and Pinecone DevOps MLOps Handson experience with DevOps MLOps practices and tools such as Git Docker Kubernetes CI CD pipelines MLflow Terraform Azure Monitor and Application Insights Cloud Security Integration Understanding of cloud security identity and access management data privacy encryption logging monitoring and secure API integration patterns AI Ethics Governance Awareness of ethical considerations and responsible AI practices including fairness accountability transparency bias detection hallucination mitigation and compliance in AI systems