Role overview
About us:
Working at Tech Holding isn't just a job, it's an opportunity to be a part of something bigger. We are a full-service consulting firm that was founded on the premise of delivering predictable outcomes and high-quality solutions to our clients. Our founders and team members have industry experience and have held senior positions in a wide variety of companies – from emerging startups to large Fortune 50 firms – and we have taken our combined experiences and developed a unique approach that is supported by the principles of deep expertise, integrity, transparency, and dependability.
The Role:
We are seeking a talented Machine Learning Engineer, for a project-based assignment, with 5+ years of experience to join our growing engineering team. In this role, you will design, build, and deploy machine learning models that power data-driven products and insights across the organization. You will work at the intersection of machine learning, data engineering, and scalable systems to deliver reliable and high-performing ML solutions.
What you'll work on
- Design, develop, and deploy machine learning models to solve business problems across large-scale datasets.
- Build and optimize machine learning pipelines for data preparation, model training, and inference.
- Collaborate with data engineers and software engineers to develop scalable ML infrastructure and pipelines.
- Research and implement modern machine learning techniques, including deep learning and large language models where appropriate.
- Work closely with product and cross-functional teams to translate business requirements into technical solutions.
- Deploy and maintain machine learning models in production environments.
- Monitor model performance, conduct experiments and A/B testing, and continuously improve model accuracy and reliability.
- Contribute to the team's engineering best practices, including code reviews, documentation, and knowledge sharing.
What we're looking for
- Experience working with natural language processing, computer vision, recommendation systems, or other applied ML domains.
- Familiarity with model deployment, experiment tracking, and model monitoring tools.
- Experience working with distributed systems and scalable ML infrastructure.
- Exposure to modern techniques such as transformer architectures, embeddings, or large language models.
- Experience working in a fast-paced startup or product-driven environment.