Olusegun Ajose

Results-driven AI Software Engineer with 3+ years of experience in software development (Python, C#, SQL) and almost 2 years of experience in machine learning, data science and data engineering. Hands-on experience in using software engineering principles to develop and deploy ML models and data pipelines at Danone, Alstom, AiMergence, Bunqr, Joxia Creative and Appzone. Bridges the gap between IT initiatives and business goals.

Ruffin Galactic, Atlanta US (Remote) | MACHINE LEARNING ENGINEER                         JUL 2023 – PRESENT

• Developed and deployed a machine learning pipeline (machine failure prediction model) using Vertex AI SDK for Python and Sklearn. 
• Created a deployment script to automate the deployment of Anything LLM (based on Pinecone and OpenAI GPT) to Digital Ocean Droplets (Ubuntu VM).
• Scheduled daily CRM data ingestion into BigQuery partition tables using Dagster data orchestrator, increasing data accuracy for analysis and decision-making. 
• Used Dagster and Airbyte to orchestrate a data pipeline for internal reporting with Bigquery as the data warehouse (instantly.ai, expandi.io and Google Analytics.
• Used Cloud Dataprep to orchestrate transformation jobs for an ecommerce dataset containing Google Analytics session records for a Merchandise client, with BigQuery as the data warehouse.

Tech Stack: GCP (Vertex AI, BigQuery, Dataflow), Digital Ocean, Python, Django, Github, Metabase, Dagster, Airbyte, Streamlit, MLFlow

Danone, Paris | AI SOFTWARE DEVELOPER – Work Study                                                 OCT 2022 – SEP 2023

Artificial Intelligence

• Developed and pitched 4 conversational Artificial Intelligence (AI) ideas/demos to business teams and innovation coaches in France, Poland and Netherlands.
• AI Product Management - discovery, client interview, strategy, budget, and delivery.
• Saved cost and time using parameter pruning and quantization techniques like PEFT and QLora to fine-tune Llama 2 and Flan-T5 – reduced model training and time cost.
• Instruct fine-tuned Llama 2 to generate question and answer dataset from unstructured free-text data for NLP projects (PEFT and QLora).
• Implemented in-context and zero-shot learning techniques to enable pre-trained models to generalize and perform tasks outside of its training data.
• Developed custom Python code to connect a conversational AI application with Azure OpenAI, AWS Kendra, Sagemaker Endpoints, HuggingFace Transformers, Slack and Langchain, enabling seamless communication between different services.

Data Engineering 

• Developed a pipeline for digital asset data using AWS cloud services (Lambda, ECR, Fargate, S3) and Docker – reducing data processing time from 2 weeks to a few hours.
• Used Python multithreading to parallelize resource-intensive operations, enabling the application to handle concurrent ETL tasks more effectively. 
• Wrote unit tests and conducted thorough code reviews to maintain code quality.
• Implemented a CI/CD pipeline GitHub Actions, Docker, and ECR (managed Kubernetes) with SSO access.

Tech Stack: AWS (ECR, ECS, Lambda, s3), Azure Open AI, Power BI, Python, LangChain, Slack Bolt, Huggingface, Streamlit, Github, Docker

Alstom, Paris | DATA SCIENTIST (NLP) - Internship                                                             MAY 2022 – SEP 2022

• Used Sentence (sBert) Transformers (Hugging Face) to extract technical data from train documentation, resulting in a significant reduction in manual data processing time.
• Implemented an NLP-driven search system using Huggingface's Transformers to improve technical document retrieval accuracy.
• Enabled engineers to focus on critical tasks by fine-tuning a summarization model to condense lengthy technical documents.

Tech Stack: Dataiku, Python, Huggingface

Aivancity, Paris | ANALYTICS ENGINEER (Various Internships)                                           JAN 2022 – SEP 2022

• Conducted thorough analysis of business needs in data analysis and reporting, utilizing tools such as Power BI to visualize and present data-driven insights effectively.
• Developed a real-time data streaming pipeline from IoT (Raspberry Pi) to AWS Kinesis to power the continuous monitoring of beehive health, harnessing live data insights to ensure the well-being of bees.
• Developed and deployed a Quality Control (Deep Learning) Application with Streamlit, Docker, AWS Elastic Beanstalk, Yolov8, Roboflow and Meta SAM model.
• Provided mentorship and guidance to junior colleagues.

Tech Stack: Azure, AWS (Kinesis, EBS), NLP, Python, Sklearn, Streamlit, Github, Docker, Yolov8, Plotly, Spark

Masters, Artificial Intelligence & Data Science     

 Aivancity School For Technology, Paris, France       2023

Masters, Information Technology                           

National Open University of Nigeria (NOUN)           2021

BSc, Electronics and Computer Engineering         

Lagos State University, Nigeria                                  2013