About Slawomir Jasinski

logoSLAWOMIR (Slav) JASINSKI

EXPERTISE

Forward-thinking Solutions Architect specializing in seamless cloud migration. With experience working on 3 continents, he expertly leads client infrastructure transitions to the cloud, optimizing systems for peak performance and security. His work in deploying recommendation systems for major European media brands demonstrates his ability to scope tailored solutions and streamline processes for enhanced delivery.

With a rich background in software development, Slawomir excels across a range of digital technologies. His work with top Australian brands like Commonwealth Bank , American Express , Skoda and Telstra , alongside European brands such as MAN Trucks, JYSK, underscores his capability to tackle unconventional challenges and drive innovation.

Renowned for his problem-solving skills and technical acumen, Slawomir thrives in the evolving technology landscape. His commitment to excellence and passion for digital transformation ensure sustainable, forward-thinking solutions that exceed client expectations.

QUALIFICATIONS

Slawomir has a Bachelor degree in Marketing and Management from the College of Management and Finance in Wroclaw, Poland. His education is supported by eighteen years experience in the digital industry on broad range roles.

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AI Expertise

  1. Prompt Engineering for ChatGPT Developed and optimized conversational prompts to enhance user engagement and improve the performance of ChatGPT models in customer service applications.
  2. Generative AI with Large Language Models Worked on projects involving text generation, summarization, and translation using state-of-the-art language models like GPT-3 and BERT.
  3. Machine Learning Techniques Implemented various NLP and machine learning techniques such as sentiment analysis, text classification, and clustering algorithms for diverse business applications.
  4. AWS Bedrock Experience Utilized AWS Bedrock for scalable machine learning solutions, focusing on data engineering, model training, and deployment in a cloud-based environment.
  5. Self-Hosted AI Models for Data Processing Built and deployed self-hosted machine learning models for real-time data processing, including data cleaning, transformation, and feature extraction.
  6. Cross-Disciplinary AI Applications Collaborated with teams across different domains to integrate AI solutions into existing systems, enhancing efficiency and automating manual processes.

Certificates

AI

GO

AWS

Kubernetes

Soft