Generative AI Engineer

Engineering Expertise

As a Generative AI Engineer, I specialize in designing, training, and deploying advanced neural architectures that create original content across text, images, and multimedia formats. My engineering approach merges deep learning frameworks with full-stack development, ensuring AI models integrate seamlessly into production environments. With extensive experience optimizing complex digital systems, I build robust pipelines that handle data preprocessing, model training, and real-time inference at scale.

Throughout my career managing high-performance marketing platforms, I developed strong systems architecture skills that directly translate to AI engineering. From creating penalty-free websites that outperform industry leaders to engineering automated content systems, my foundation in performance optimization gives me unique insight into building efficient, scalable AI infrastructure.

Technical Architecture

Professional Experience

AI Systems Architect | TuneMyWebsite | 2015 – Present
Engineered comprehensive AI pipelines that automate content generation, SEO optimization, and performance analytics across European markets. Developed modular neural architectures capable of generating multilingual marketing assets while maintaining brand consistency and search performance. Built data ingestion systems that process competitor intelligence, market trends, and user behavior into actionable model inputs.

Machine Learning Engineer | Freelance AI Projects | 2020 – 2025
Designed custom generative models for e-commerce platforms, publishing houses, and digital agencies. Implemented Retrieval-Augmented Generation systems combining vector search with language models for contextually accurate content creation. Engineered real-time personalization engines that adapt content delivery based on user interaction patterns and predictive analytics.

Digital Systems Manager | Unboxed 2022 | UK Government Project
Architected cross-functional AI infrastructure supporting content teams, analytics, and performance monitoring. Developed predictive models for resource allocation and workflow optimization across multiple digital departments. Implemented early machine learning systems for automated quality assurance and content personalization at scale.

Performance Engineering Lead | Multiple Marketing Roles | 2016-2022
Created intelligent bidding systems and ad optimization algorithms that reduced cost-per-click by up to 90% compared to industry benchmarks. Engineered automated A/B testing frameworks and performance dashboards that processed millions of data points daily to inform strategic decisions.

Engineering Projects

Multilingual Content Generation Platform
Built transformer-based models trained on English, French, Italian, Spanish, and other language datasets to generate SEO-optimized content. Implemented cross-lingual transfer learning techniques enabling models to adapt to new languages with minimal retraining. Deployed as scalable microservices handling 10,000+ daily generations across global markets.

Competitive Intelligence Engine
Developed neural network architecture that processes unstructured competitor data, market reports, and SERP analysis into structured embeddings. Created recommendation systems suggesting content gaps, keyword opportunities, and strategic positioning based on real-time competitive landscapes. System powers automated research workflows reducing manual analysis by 85%.

Adaptive Creative Generation
Engineered multimodal AI system combining text generation with image synthesis capabilities. Models understand brand guidelines, audience demographics, and performance metrics to generate complete campaign assets. Integrated real-time A/B testing feedback loops enabling continuous creative optimization.

Development Stack

Proficient across the complete AI engineering lifecycle from data pipeline construction through production deployment. Primary stack includes Python for model development, TensorFlow/PyTorch for deep learning, FastAPI for service creation, and Docker/Kubernetes for container orchestration. Experienced with vector databases (Pinecone, Weaviate) for semantic search and cloud platforms (Google Cloud Platform, AWS) for scalable inference.

Strong full-stack foundation with HTML/CSS/JavaScript, Drupal/Bootstrap for front-end interfaces, and database systems for persistent storage. Performance engineering background ensures models achieve sub-second inference times even at enterprise scale. Extensive DevOps experience managing CI/CD pipelines, automated testing, and production monitoring.

Key Engineering Achievements

• Engineered AI systems processing 1M+ data points daily for real-time performance optimization
• Reduced inference latency by 87% through model quantization and caching strategies
• Developed multilingual models supporting 8+ languages with 92% cross-lingual accuracy
• Architected containerized deployment reducing infrastructure costs by 65%
• Implemented automated retraining pipelines cutting model maintenance time by 78%
• Created production systems serving 50K+ daily inference requests with 99.9% uptime

Academic Foundation

Bachelor's in Multimedia Computing provides strong theoretical grounding in algorithms, data structures, and systems design. Extensive self-study in machine learning theory, optimization techniques, and neural architecture research. Multiple industry certifications validate practical engineering expertise across AI frameworks, cloud platforms, and performance analytics systems.

Engineering Philosophy

Production-grade AI engineering demands balancing model sophistication with deployment practicality. Every architecture decision weighs inference speed, memory footprint, and scalability requirements. Systems thinking guides end-to-end design from data ingestion through user interaction, ensuring cohesive intelligent experiences rather than isolated components.

Rigorous testing frameworks validate model behavior across edge cases, cultural contexts, and performance conditions. Continuous monitoring and automated retraining maintain model relevance as data distributions evolve. Ethical considerations shape every deployment decision, prioritizing transparency, fairness, and human oversight in critical applications.