IBM AI-Driven Threat Detection Engineer

Role Overview The AI Engineer will be a key contributor to the client's digital transformation initiatives. This role focuses on designing, developing, and deploying scalable, ethical AI solutions to improve operational efficiency, automate decision-making, and support strategic initiatives for environmental and social programs. Experience Minimum 3+ years of hands-on experience in AI/ML model development and deployment. Strong experience with LLMs, RAG, NLP, and Generative AI solutions. Experience with MLOps, Docker, and cloud platforms (AWS, Azure, or Google Cloud Platform). Key Responsibilities AI Solution Development Design, develop, train, validate, and optimize AI/ML models. Implement Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), NLP models, and GenAI solutions. Translate business requirements into technical specifications. AI Solution Implementation & Operations Deploy AI models into production environments. Perform ongoing model training, monitoring, troubleshooting, and fine-tuning. Manage MLOps pipelines using tools such as MLflow and Docker. Ensure compliance with ethical AI standards and data governance policies. Data Analysis & Research Collect, preprocess, and engineer large structured and unstructured datasets (including geospatial data). Recommend and implement data cleaning and preparation strategies. Monitor data quality, detect bias, and manage model/data drift. Research emerging AI technologies and recommend enhancements. User Enablement & Documentation Conduct training sessions for stakeholders. Develop technical documentation and user guides to support adoption. Governance, Strategy & Support Advise World Bank Group staff on AI strategy and policy implications. Contribute to AI roadmap, innovation agenda, and long-term enhancements. Provide continuous support and maintenance for existing AI solutions. Required Qualifications Education Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field. Technical Skills Proficiency in Python, TensorFlow, PyTorch, and NLP frameworks. Experience with Azure AI, Google Vertex AI/Search, and Vector Databases. Strong knowledge of deep learning, computer vision, and large language models. Experience with REST APIs, NoSQL, and RDBMS. API Management experience for integrating multiple data sources. Preferred Certifications Microsoft Certified: Azure AI Engineer Associate Google Machine Learning Engineer SAFe Agile Software Engineer (ASE) Certification in AI Ethics Deliverables / Milestones Develop Proof of Concepts (POCs) to assess feasibility of AI use cases Develop, train, and deploy AI models aligned to business objectives.

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