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Nicholas Lu

AI Engineer
Nicholas Lu is a goal-oriented engineer focused on ML systems and distributed architecture. He works across the MLOps lifecycle, building and maintaining data and model pipelines using Airflow, Spark, Kafka, and Kubernetes. His experience includes deploying containerised workloads with Docker, managing infrastructure via Terraform, and implementing CI/CD with Jenkins, GitLab, and Bitbucket Pipelines. He supports site reliability through monitoring, automation, and incident response in cloud environments (AWS, GCP, Azure, Alibaba Cloud). He codes in Python, Java, SQL, and Bash, and works extensively with Linux and open-source tools.

Anni Huang

AI Engineer
Anni Huang specialises in artificial intelligence with a strong focus on algorithms, machine learning, and deep learning applied to real-world industrial challenges. Her work spans end-to-end data science pipelines, model development, and deployment, with particular emphasis on MLOps practices. She builds scalable workflows on Google Cloud Platform and is advancing her MLOps capabilities on AWS, covering model versioning, CI/CD, monitoring, and reproducibility. She is proficient in software development, data visualisation using R and Tableau, and translating complex models into production-ready solutions.

Chris Simpson

AI Engineer
Worked at Syngenta and Charter.
Chris Simpson has been a web developer (HTML, CSS, and JavaScript) for 16 years. His first 8 years were full-stack with C# and ASP.Net, and the last 8 years have been front-end with jQuery, Angular.js, and Vue.js. Most of his web development experience has been with internal tools within Telecommunications, Healthcare, and Biotech. He likes to work with AI, IoT, AU, Blockchain, VR, robots or 3D printing; and/or within software, electronics, material science, sensing, biotech, green tech, frontier research or military tech. He enjoyed doing R&D and prototyping more than production work.

Laura Halacheva

AI Engineer
Data science lead for Teva Pharmaceuticals.
Laura Halacheva is a data scientist with experience in data modeling, statistics, and machine learning, both theoretical and applied. She has wide experience with applications in various domains, including medical data analysis, natural language processing, conversational models, and e-commerce. At Ontotext, she led the data science of the R&D department. Her clients include MobiBiz, Canadian Heritage Information Network (CHIN), and more.

Tin Chu

AI Engineer
With experience in generative AI, WordPress, and full-stack development.
Tin Chu is a bilingual frontend developer with over eight years of experience in WordPress and full-stack JavaScript web development. At Red Ventures, he has designed and developed websites and microsites to support clients' business initiatives, including online design, SEO, and brand identity. His past clients include Ninjanumber by Cirravox, Riess Group, Zyper.com, UV Digital Services, Sigma Rise, New York City Spine, King.com, and Mandaê.

Peter Faasse

AI Engineer
Former data science manager for Career Analytics.
As a data scientist, Peter Faase has expertise in solving complicated data problems and working with Big Data to generate useful insights, intelligence, and applications. Combining his strong communication, presentation, and analytical skills, he has created data-driven solutions for organizations by designing and running complex data analyses trajectories using machine learning, predictive analyses, Artificial Intelligence (AI), and other statistical and mathematical techniques. He is skilled with tools such as Python, MySQL, SPSS, and Excel and also familiar with Agile Scrum techniques.
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Frequently asked questions

Hire Digital connects companies with talent in marketing, digital, and content creation. We curate our expert network through a rigorous screening process, where members are evaluated for their technical skills, work experience, and their problem solving and communication skills.
We’ll match the best talent for your needs based on the required skills, expertise level, project size, budget, language, geography, experience, and other criteria. A decision is made by our experts with the help of AI-based algorithms. Only 3% of the talent we vet are accepted to our network - hence you can be assured of the quality.
Hire Digital provides satisfaction guarantees for its clients. If the talent does not perform up to your standard, Hire Digital will provide a talent replacement.
Tell Hire Digital your requirements and talent needs, or let us know by registering here. Our talent specialists will curate and find you the most relevant match. You can interview your talent and start work immediately.
Companies typically take between 24 to 72 hours from the time when they inform Hire Digital of their talent needs, to start working with their matched talent.
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