AI Architect

October 31

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SDG Group

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Business Analytics • Corporate Performance Management • Business Insights • Advanced Analytics • Data Management & Engineering

1001 - 5000

Description

• Lead Innovation: Drive teams of machine learning engineers to implement groundbreaking AI and Generative AI solutions in large-scale enterprise environments. • Design the Future: Architect advanced cloud analytics platforms that span the entire data science lifecycle, with a focus on AIOps best practices. • Shape Strategy: Help our clients craft robust strategies and methodologies for leveraging data science and machine learning in cloud ecosystems. • Master Cloud Technologies: Work with leading tools and platforms like Azure Databricks, Azure Machine Learning, Amazon SageMaker, Google Vertex AI, and more to develop cutting-edge solutions. • Build Our Vision: Collaborate to define SDG’s value proposition in data science, particularly around next-gen architectures for machine learning systems. • Create Impact: Work closely with consulting and sales teams to craft compelling business proposals, bringing your solution architecture expertise to every project. • AI Systems Tech Architecture Solution Design: Designing technical architecture solutions for AI systems, leveraging frameworks and best practices from vendors or hyperscalers, or drawing on our extensive engineering experience, to create robust scalable AI/ML systems that ensure seamless integration and performance. • AI Digital Twin Foundation: Building a robust foundation with a composable architecture of AI and ML systems, rule-based systems, and cognitive knowledge chains enhanced by generative AI. This enables real-time monitoring, simulation, and optimization of virtual representations of physical systems for enhanced operational efficiency.

Requirements

• A degree in Computer Science, Telecommunications Engineering, or a related technical field with a strong background in software engineering. • 8+ years of professional experience, with at least 5 years specializing as a machine learning engineer or machine learning architect. • Deep theoretical and practical expertise in data science fundamentals, solution lifecycles, and AIOps methodologies. • Strong background in software development and AI implementation. • Proven experience designing and building scalable architectures for machine learning platforms in the cloud. • Advanced knowledge of state-of-the-art cloud technologies and platforms for AI, including Azure, AWS, and Google Cloud. • Excellent communication and presentation skills, with the ability to explain complex concepts to both technical and non-technical audiences. • A Master’s or PhD in Big Data, Artificial Intelligence, or related fields (preferred qualification). • Strong understanding of data architectures and information modeling strategies (preferred qualification). • Previous consulting experience in Data & Analytics (preferred qualification). • Experience as a data engineer in analytics environments like data warehouses or data lakes (preferred qualification). • Expertise in cloud-based data processing architectures and automation solutions (preferred qualification). • Hands-on experience with Generative AI models, including Large Language Models (LLMs), neural networks, and recommendation systems (preferred qualification). • Proficiency with tools and frameworks like TensorFlow, PyTorch, Hugging Face, and OpenAI (preferred qualification). • Certifications in cloud platforms like AWS, Azure, or Google Cloud, with a focus on machine learning architecture (preferred qualification).