Fractional CTO for Generative AI
From traditional ML to RAG pipelines and agentic workflows, I help startups and SMEs craft GenAI solutions that are scalable and tailored to their unique needs.
Book a Free ConsultationWhy GenAI for Your Business?
GenAI is revolutionizing industries by automating processes, enhancing decision-making, and creating innovative customer experiences. Here’s how I can help you leverage it:
- Integrate AI-driven solutions to enhance your product offerings and reduce operational costs.
- Develop scalable architectures to handle AI workloads and adapt to evolving needs.
- Strategize and implement AI applications that align with your business goals.
With decades of experience in app development and cloud solutions, I offer the expertise to turn your vision into reality.
AI Strategy: From ML to Foundational Models
Navigating the rapidly evolving AI landscape can be daunting. I guide my clients in choosing the right AI approach for their needs, starting with the easiest and most cost-effective options to validate before scaling further.
- Traditional Machine Learning: Best for structured problems with clearly defined datasets.
- Foundational Models: Suitable for scenarios requiring robust natural language understanding and generation.
- Retrieval-Augmented Generation (RAG): Ideal for integrating large-scale knowledge bases with generative capabilities.
- Agentic Workflows: Automating complex decision-making tasks using AI agents.
- Fine-Tuning and Distilling: Optimizing models for specific business needs while reducing infrastructure costs.
My goal is to ensure every project starts lean, validates quickly, and scales intelligently, saving costs and delivering value faster.
The Role of the AI Engineer
The emergence of foundation models has transformed the landscape of AI development, shifting the focus from model creation to application development. AI engineers are now at the forefront of adapting and integrating these powerful models into innovative products that drive business outcomes.
Key Differentiators from ML Engineering:
- Model Adaptation vs. Development: AI engineers focus on fine-tuning and integrating existing models rather than creating them from scratch.
- Unstructured Data Mastery: Work involves deduplication, tokenization, context retrieval, and ensuring data quality, unlike traditional ML's emphasis on feature engineering with tabular data.
- Differentiation Through Applications: Success is achieved by innovating in application interfaces and workflows rather than solely relying on proprietary model quality.
- Closer to Full-Stack Development: AI engineers often come from web or full-stack backgrounds, bringing a product-first mindset and rapid prototyping skills.
- Product-First Approach: Foundation models enable teams to focus on building the product first and only investing in custom data and models once the product shows promise.
As an AI engineer, I bring expertise in navigating this evolving landscape, ensuring a lean, iterative approach to validate ideas and scale intelligently. Whether you're starting with traditional ML or exploring cutting-edge applications with foundation models, I guide you every step of the way.
Case Study: Generative AI Innovation for a FinOps Startup
As a Technical Lead at Xogito Group, I spearheaded the development of a state-of-the-art chatbot for a FinOps startup in Seattle. The chatbot integrated cost attribution, budgeting, monitoring, and optimization for generative AI services, showcasing platform capabilities.
Delivered an MVP in just one month by leveraging pre-existing front-end components and developing a containerized backend—drastically reducing time to market. This rapid delivery was key in helping the startup attract new customers, demonstrating the value of its generative AI cost management solutions.
Key Benefits:
- Rapid time-to-market for the MVP helped attract investors and customers early in the product lifecycle.
- Leveraged existing components and containerized infrastructure to minimize development costs.
- Integrated advanced AI cost optimization features, positioning the platform as an innovator in the FinOps space.
- Created a scalable backend that supports future growth without the need for a full system redesign.
Let’s Build Your GenAI Strategy
From traditional ML to RAG pipelines and agentic workflows, I'll guide you through designing the best, most cost-effective way to validate and implement your vision.