Artificial Intelligence Architect

Artificial Intelligence Architect

Work Mode: Hybrid

Opportunity

The AI Architect specializes in designing, developing and implementing end to end data science and ML solutions including Statistical Modeling, Machine Learning, Deep Learning and Generative AI as well as establishing product lifecycle processes like MLOPS and AIOPS.  The AI architect will also oversee the technical architecture of AI systems, establishing best practices for model training, deployment, monitoring, and maintenance. As a Senior member of the team, the AI architect will also review the work of team members, drive essential team functions like design and code reviews, and mentor team members to adopt best practices in AI solution development.

Specific Responsibilities

•Solution Design: Design end-to-end AI solutions that address specific business problems in Finance, Marketing, Supply Chain and Digital experiences, including selecting appropriate algorithms, models, and technologies that aptly serve the problem statements
•Technical Leadership: Provide technical expertise and guidance to AI Data Scientists, ensuring alignment with coding standards, Industry standard algorithms, and architectural principles.
•System Architecture: Define the overall architecture of AI systems, including data pipelines, model training, inference engines, and integration with existing IT infrastructure.
•Model Development: Oversee the development and optimization of machine learning models, ensuring high performance, scalability, and interpretability.
•Technology Evaluation: Evaluate new AI technologies, tools, and frameworks to recommend the most suitable options for specific use cases and project requirements.
•Collaboration: Partner with Product managers to decide on fit for purpose model architectures that serve business use cases and contribute to the product road map development. The Architect will also effectively communicate with product managers and stakeholders, with a focus on simplifying complex modeling approaches in a manner understood by all.
•Collaboration Cross-functionally:  Work closely with cross-functional teams, including subject matter experts, data scientists, data engineers, and software developers, to integrate AI capabilities into various applications and processes
•GenAI Solution Design: Utilize your expertise in GenAI frameworks to architect scalable, efficient, and high-performance AI solutions that leverage the latest advancements in the field.
•MLOps and AI Ops Implementation: Implement MLOps and AI Ops practices to streamline the machine learning and LLM lifecycle, including version control, automation, CI/CD pipelines, and monitoring for AI models.
•Research & Development: Stay abreast of industry trends, emerging technologies, and research breakthroughs to continuously enhance the performance and capabilities of AI solutions.
•Performance and Cost Optimization: Optimize AI models for speed, accuracy, and efficiency through techniques such as hyperparameter tuning, model compression, and deployment optimizations. Bring in cost-effective solutions that are scalable and reliable.
•Security & Compliance: Ensure that AI systems developed adhere to data protection regulations, security protocols, and ethical standards in AI development and deployment, collaborating with EAO, Security and Cloud Infra teams.