Lead AI Engineer

Project description

As the AI Engineer within the Data Analytics & Artificial Intelligence division, you will design, develop, and deploy production-grade 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 end-to-end AI solutions. 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 Banks GERNAS OS platform and Agent Development Kit (ADK).

Responsibilities

• AI Solution Design & Development: Design, build, and deploy AI-powered applications using Azure AI Services, including Azure OpenAI Service, Azure Machine Learning, Azure Cognitive Services (Speech, Vision, Language), Azure AI Search, Azure Functions, and Azure Databricks.
• LLM Integration & Generative AI: Develop and integrate Large Language Model (LLM) solutions using Azure OpenAI endpoints (GPT-4.1, GPT-4o, GPT-4o-mini) routed through FAB's centralized AI Hub gateway for governance, observability, and capacity management. Build enterprise use cases such as knowledge search, document intelligence, content summarization, call analytics, sentiment analysis, and workflow automation.
• Python Engineering & Backend Development: Write clean, modular, and production-grade Python code for AI/ML model development, API integrations, data processing pipelines, backend services, and automation workflows using frameworks such as LangChain, LlamaIndex, Semantic Kernel, LangGraph, and FastAPI.
• RAG & Semantic Retrieval: Implement Retrieval-Augmented Generation (RAG) pipelines, embeddings, vector search, semantic retrieval architectures, prompt engineering, and evaluation techniques for enterprise knowledge mining and document intelligence.
• Agentic AI Development: Design and develop intelligent agentic AI systems capable of planning, reasoning, tool execution, and orchestration across enterprise systems using FAB's GERNAS OS platform and Agent Development Kit (ADK) in a secure and governed manner.
• Model Evaluation & Responsible AI: Implement model evaluation metrics for accuracy, hallucination detection, bias, latency, and throughput in alignment with FAB's Responsible AI framework. Ensure AI solutions comply with FAB's security, data privacy, model governance, and regulatory requirements, including controls for accuracy, hallucination, bias, latency, and resilience.
• Deployment & Production Operations: Deploy, monitor, and optimize AI solutions in production environments using CI/CD practices, containerization, observability, logging, performance monitoring, and cost optimization techniques.
• Collaboration & Stakeholder Engagement: Collaborate with data scientists, platform engineers, DevOps/MLOps engineers, business analysts, and product teams to convert business requirements into scalable AI solutions.
• Innovation & Continuous Learning: Continuously evaluate emerging Azure AI, Generative AI, and Agentic AI capabilities and recommend practical adoption opportunities aligned with FAB's AI strategy. Stay abreast of state-of-the-art developments in generative AI and agentic architectures

SKILLS

Must have

• Experience: total 6 to 10+ years in software field with minimum 3+ years of hands-on experience in designing, developing, and deploying AI/ML or Generative AI solutions in production environments.
• Minimum 3-4 yrs of working experience mandatory on below technical skills sets:
• 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, scikit-learn, and NLP libraries (spaCy, NLTK).
• Vector Databases: Experience with vector databases including FAISS, Azure AI Search, ChromaDB, and Pinecone.
• DevOps/MLOps: Hands-on 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.

Nice to have

NA