Machine Learning Specialist

Position Title:      Machine Learning Specialist (Research & Engineering)

Work Location:    BGC, Taguig City. (2 x onsite per week  hybrid set up)

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.

Key Responsibilities

The following are key areas of responsibility, but not limited to the ff:

1. Research & Experimental Innovation

• Advanced Research: Conduct deep-dive research into state-of-the-art (SOTA) architectures and foundational models to solve complex business problems like credit scoring, fraud detection, and personalization.
• Model Optimization: Execute rigorous hyperparameter tuning and fine-tuning techniques (e.g., PEFT, LoRA, QLoRA) to maximize model accuracy and efficiency.
• Benchmarking & Evaluation: Develop comprehensive evaluation frameworks and leaderboards to monitor model accuracy and compare experimental iterations.

1. Data Strategy & Engineering

• Pipeline Design: Lead the design of experimentation datasets and production data pipelines, focusing on feature engineering and data augmentation.
• Data Quality: Ensure high-quality data inputs for both training and real-time inference, collaborating with data squads to maintain data integrity.

1. Production Engineering & MLOps

• Deployment & Orchestration: Architect and manage the end-to-end deployment of models using containers (Docker, Kubernetes) and CI/CD pipelines.
• System Integration: Build robust APIs to integrate AI models with internal platforms and refactor research code into production-grade, low-latency, and high-throughput codebases.
• Model Governance: Implement MLOps best practices, including versioning (DVC), drift detection, and automated "quality gates" to ensure alignment with internal KPIs and regulatory standards.

1. Squad Collaboration & Agile Delivery

• Active Squad Collaboration: Work as a core member of a cross-functional squad, aligning daily with Data Engineers, Backend Developers, and Product Owners to ensure seamless product integration.