We are seeking a versatile
Machine Learning Specialist to own the end-to-end lifecycle of AI development. This role is designed for a technical expert who can navigate the entire spectrum of machine learning—from conducting state-of-the-art research and fine-tuning foundational models to architecting the production-grade pipelines and APIs that bring these models to life. You will bridge the gap between theoretical innovation and scalable business impact, ensuring our AI solutions are both cutting-edge and operationally robust.
Roles & Responsibilities
- Conduct deep-dive research into state-of-the-art architectures and foundational models to solve complex business problems.
- Execute rigorous hyperparameter tuning and fine-tuning techniques to maximize model accuracy and efficiency.
- Develop comprehensive evaluation frameworks and leaderboards to monitor model accuracy and compare experimental iterations.
- Lead the design of experimentation datasets and production data pipelines, focusing on feature engineering and data augmentation.
- Ensure high-quality data inputs for both training and real-time inference, collaborating with data squads to maintain data integrity.
- Architect and manage the end-to-end deployment of models using containers and CI/CD pipelines.
- Build robust APIs to integrate AI models with internal platforms and refactor research code into production-grade codebases.
- Implement MLOps best practices, including versioning, drift detection, and automated quality gates.
- Work as a core member of a cross-functional squad, aligning daily with Data Engineers, Backend Developers, and Product Owners.
- Drive technical value within Agile ceremonies by translating high-level business requirements into executable research hypotheses.
- Author and maintain the full technical stack documentation, ranging from scientific research findings to deployment guides.
- Act as a technical subject matter expert by mentoring squad members and fostering an internal culture of AI literacy.
Required Qualifications
- Graduate with the degree in a quantitative field (e.g., Computer Science, Statistics, Information Technology, Physics, or Mathematics). A Graduate degree (Master's or PhD) is highly preferred.
- 3+ years in a functionally similar role (Data Science, ML Research, or ML Engineering).
- Expert-level Python and SQL.
- Strong experience with ML frameworks (e.g., PyTorch, TensorFlow, JAX).
- Hands-on experience with Git, CI/CD, and MLOps tools.
- A strong bias toward model explainability and security.
- A demonstrable portfolio of advanced AI use cases (e.g., GenAI, NLP, Recommender Systems, or Graph Algorithms).
- Familiarity with AWS, GCP, or Azure AI services.
- Published research in relevant AI/ML conferences or journals.
- Experience with data visualization tools for model performance monitoring.
- Knowledge of ethical AI practices and compliance standards.
