Job description

Position Responsibilities: - Solution Design & Architecture: Design and architect end-to-end AI/ML solutions, ensuring they align with business objectives and scalability requirements; define overall architecture and data pipelines, selecting appropriate AI tools and frameworks for each project; ensure best practices are applied in security, scalability, and maintainability of AI systems. - Development & Deployment: Build, test, and deploy AI and machine learning models on Azure platforms (Azure ML, Cognitive Services, Azure Data Factory, Azure Synapse), or other best of industry solutions; develop APIs and integration points for seamless data exchange between AI solutions and existing business systems (e.g., Internal Systems/Databases, Microsoft Dynamics 365, SharePoint, Power BI); implement CI/CD pipelines, containerization (Docker, Kubernetes), and automated testing to ensure efficient deployment and monitoring of AI models. - Cloud Infrastructure & MLOps: Establish MLOps best practices for continuous integration, delivery, and monitoring of models; optimize AI model performance for scalability and low latency in production; implement solutions using infrastructure-as-code principles and tools to enhance reliability and scalability. - Collaboration & Consultation: Work closely with tax, audit, and advisory teams to identify use cases where AI can add value; translate domain requirements into technical specifications and AI solutions; provide technical guidance and mentorship to junior AI engineers and developers. - Research & Continuous Improvement: Stay current with advancements in AI; experiment with and implement new AI techniques and tools to enhance service offerings; develop Proof of Concepts (POCs) for innovative AI solutions. - Documentation & Support: Create and maintain comprehensive documentation for AI models, data pipelines, and system architecture; ensure AI solutions comply with data privacy, security policies, and ethical AI guidelines; provide training and support to other developers and end-users to increase AI literacy. Technical Qualifications: - Proven experience in AI architecture, design, and deployment with Microsoft Azure Cloud Technologies, or similar industry best technologies (Azure ML, Cognitive Services, Azure Data Factory, Azure Kubernetes Service, etc.). - Proficiency in programming languages such as C#, Python, R, or JavaScript. - Strong understanding of machine learning frameworks and libraries (e.g., PyTorch, TensorFlow, scikit-learn). - Familiarity with Microsoft Power Platform (Power BI, Power Apps, Power Automate) and integrating AI models with these tools. - Experience with DevOps tools and practices including CI/CD, containerization (Docker, Kubernetes), and automated testing. - Knowledge of data architecture, databases, and data lakes (SQL, NoSQL, Azure Synapse, etc.). - Knowledge and experience with vector databases or knowledge bases for retrieval-augmented generation. - Knowledge and experience of prompt engineering and fine-tuning large models. Other Skills: - Excellent analytical, problem-solving, and decision-making abilities. - Strong communication skills, with the ability to translate complex technical concepts to non-technical stakeholders. - Collaboration and teamwork skills with a proactive, innovative mindset. Education & Experience: - Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field. - 5+ years of experience in AI/ML development, architecture, and deployment. - 3+ years of experience working with Microsoft Azure technologies preferred. - Certifications in Microsoft Azure (e.g., Azure AI Engineer Associate, Azure Solutions Architect Expert, or Azure Data Scientist Associate) preferred. - Experience with AI governance, ethics, and compliance in enterprise environments preferred. - Experience with generative AI frameworks and tools such as LangChain, Semantic Kernel, or other LLM orchestration tools preferred. - Experience in the accounting or financial services industry is a plus.

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