Machine Learning Specialist (Research and Engineering)

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

1. Conduct deep-dive research into state-of-the-art architectures and foundational models to solve complex business problems.
2. Execute rigorous hyperparameter tuning and fine-tuning techniques to maximize model accuracy and efficiency.
3. Develop comprehensive evaluation frameworks and leaderboards to monitor model accuracy and compare experimental iterations.
4. Lead the design of experimentation datasets and production data pipelines, focusing on feature engineering and data augmentation.
5. Ensure high-quality data inputs for both training and real-time inference, collaborating with data squads to maintain data integrity.
6. Architect and manage the end-to-end deployment of models using containers and CI/CD pipelines.
7. Build robust APIs to integrate AI models with internal platforms and refactor research code into production-grade codebases.
8. Implement MLOps best practices, including versioning, drift detection, and automated quality gates.
9. Work as a core member of a cross-functional squad, aligning daily with Data Engineers, Backend Developers, and Product Owners.
10. Drive technical value within Agile ceremonies by translating high-level business requirements into executable research hypotheses.
11. Author and maintain the full technical stack documentation, ranging from scientific research findings to deployment guides.
12. Act as a technical subject matter expert by mentoring squad members and fostering an internal culture of AI literacy.

Required Qualifications

1. 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.
2. 3+ years in a functionally similar role (Data Science, ML Research, or ML Engineering).
3. Expert-level Python and SQL.
4. Strong experience with ML frameworks (e.g., PyTorch, TensorFlow, JAX).
5. Hands-on experience with Git, CI/CD, and MLOps tools.
6. A strong bias toward model explainability and security.
7. A demonstrable portfolio of advanced AI use cases (e.g., GenAI, NLP, Recommender Systems, or Graph Algorithms).
8. Familiarity with AWS, GCP, or Azure AI services.
9. Published research in relevant AI/ML conferences or journals.
10. Experience with data visualization tools for model performance monitoring.
11. Knowledge of ethical AI practices and compliance standards.