Machine Learning (ML) Engineer
As a Machine Learning Engineer at Equitech, you will be responsible for designing, building, and deploying robust machine learning models and data pipelines. You will work closely with data scientists, software engineers, and product managers to transition experimental models into production-grade systems. A key focus of this role will be ensuring that our models are not only accurate and efficient but also ethically sound and free from bias.
Key Responsibilities
Model Productionalization: Scale, optimize, and deploy machine learning models (NLP, tabular data, computer vision, or LLMs) into secure production environments.
MLOps & Pipeline Architecture: Design, build, and maintain end-to-end data and MLOps pipelines for automated data ingestion, feature engineering, model training, and continuous monitoring.
Algorithmic Fairness: Implement frameworks to detect and mitigate bias in data and model predictions, ensuring our AI systems align with Equitech’s core values of equity and transparency.
Collaboration & Integration: Collaborate with backend engineers to integrate ML model outputs into user-facing applications via clean, well-documented APIs.
Performance Optimization: Monitor and optimize model inference latency, throughput, and infrastructure costs in cloud environments (AWS/GCP/Azure).
Research & Innovation: Stay up-to-date with the latest advancements in AI/ML, evaluating and adopting new tools or methodologies that can elevate our technical stack.
Qualifications & Skills
Minimum Qualifications
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related quantitative field (or equivalent practical experience).
2–4+ years of professional experience as an ML Engineer, MLOps Engineer, or Data Scientist in a production environment.
Strong proficiency in Python and standard ML frameworks (e.g., PyTorch, TensorFlow, Scikit-Learn).
Solid experience with cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
Experience building and managing data/ML pipelines using tools like Airflow, Kubeflow, MLflow, or Prefect.
Familiarity with SQL and NoSQL databases, data warehousing, and big data technologies (e.g., Spark).
Preferred Qualifications
Experience with Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) or bias-detection toolkits (e.g., AIF360, Fairlearn).
Experience deploying and fine-tuning Large Language Models (LLMs) and working with vector databases (e.g., Pinecone, Milvus, Chroma).
Strong software engineering fundamentals, including version control (Git), CI/CD, and writing clean, testable code.
How to Apply
If you are ready to build the future of equitable technology, please submit your resume and a brief cover letter to info@equitech-hk.com.
Equitech is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.