Lead Analyst-GenAI Lead With Data Engineer

Job description

We are seeking an experienced AI / Generative AI Architect to design and implement enterprise-scale AI and Generative AI platforms. The role involves building scalable AI architectures, enabling GenAI solutions using LLMs, and establishing robust MLOps and governance frameworks. The candidate will work closely with business leaders, product teams, and engineering teams to drive AI adoption and deliver high-impact enterprise AI solutions Behavioural Competencies : - Proven experience of delivering process efficiencies and improvements • Clear and fluent English (both verbal and written) • Ability to build and maintain efficient working relationships with remote teams • Demonstrate ability to take ownership of and accountability for relevant products and services • Ability to plan, prioritise and complete your own work, whilst remaining a team player • Willingness to engage with and work in other technologies Your future duties and responsibilities AI & Enterprise Architecture Define and implement enterprise architecture for AI, Machine Learning, and Generative AI platforms. Design scalable AI systems covering data ingestion, model development, deployment, and monitoring. Architect solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and enterprise knowledge systems. Establish scalable and reusable AI platform components across the organization. Generative AI Solution Development Design and build LLM-powered applications such as: Enterprise chatbots Knowledge assistants Automation solutions Implement prompt engineering, embeddings, vector databases, and retrieval pipelines. Evaluate and integrate enterprise LLM models such as: GPT-4 Llama Claude Build RAG pipelines to integrate enterprise knowledge repositories with GenAI solutions. ML Engineering & MLOps Architect end-to-end machine learning pipelines including data preparation, model training, deployment, and monitoring. Implement MLOps frameworks and CI/CD pipelines for automated model lifecycle management. Ensure secure, scalable, and reliable model deployment across cloud and hybrid environments. Implement model monitoring, performance tracking, and retraining mechanisms. Governance, Security & Compliance Implement AI governance frameworks, model explainability, and responsible AI practices. Ensure compliance with enterprise data security, privacy, and regulatory standards. Conduct AI risk assessments aligned with financial services regulations. Collaborate with risk, compliance, and legal teams to ensure responsible AI adoption. Enterprise Integration Integrate AI solutions with enterprise platforms, APIs, data warehouses, and operational systems. Work with business stakeholders and product teams to identify high-value AI use cases. Enable AI-driven solutions that improve operational efficiency and customer experience. Leadership & Strategy Lead and mentor AI engineers, ML engineers, and data scientists. Define and drive the AI roadmap and enterprise AI strategy. Promote adoption of Generative AI capabilities across business units. Drive innovation, best practices, and technology standards for AI development.