Machine Learning Specialist

Job description

**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. - Agile Participation: Drive technical value within Agile ceremonies (Stand-ups, Sprints, Retrospectives) by translating high-level business requirements into executable research hypotheses and production-ready sprints. 1. Documentation & Knowledge Leadership - Technical Documentation: Author and maintain the full technical stack documentation, ranging from scientific research findings and experimental logs to system architecture diagrams and deployment guides. - Peer Mentoring: Act as a technical subject matter expert by mentoring squad members, conducting code reviews, and fostering an internal culture of AI literacy and "New Ways of Working." Minimum Requirements - Education: Undergraduate degree in a quantitative field (e.g., Computer Science, Statistics,Information Technology or Physics, or Mathematics). A Graduate degree (Master’s or PhD) is highly preferred for the research component. - Experience: 3+ years in a functionally similar role (Data Science, ML Research, or ML Engineering). - Technical Proficiency: \* 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. - Mindset: A strong bias toward model explainability and security. Preferred Skills - Portfolio: A demonstrable portfolio of advanced AI use cases (e.g., GenAI, NLP, Recommender Systems, or Graph Algorithms). - Cloud Infrastructure: Familiarity with AWS, GCP, or Azure AI services. - Publications: Published research in relevant AI/ML conferences or journals.