Machine Learning Specialist

Encora; Philippines; On-site; PHP 1,800,000 - 2,400,000; Full time

Job summary

Encora is seeking a Machine Learning Specialist to own end-to-end AI development—from research to production-grade deployment. You will work across data engineering, model development, and MLOps to deliver scalable AI solutions with robust APIs and governance.

The role requires strong Python/SQL, hands-on ML frameworks, and experience with Docker/Kubernetes. Expect a hybrid onsite arrangement in Taguig with collaboration across squads, architecture reviews, and documentation.

Qualifications

• Undergraduate degree in a quantitative field; a graduate degree is highly preferred for research.
• 3+ years in a data science, ML research, or ML engineering role.
• Proficiency in Python and SQL with hands-on ML frameworks.

Responsibilities

• Lead end-to-end ML lifecycle from research to production.
• Design experimentation datasets and production data pipelines with feature engineering and data augmentation.
• Deploy models using containers and CI/CD pipelines; build production-grade APIs.

Skills

Python

SQL

PyTorch

TensorFlow

JAX

Git

CI/CD

MLOps

Model explainability

Data pipelines

Production ML

Education

Bachelor’s degree in Computer Science, Statistics, Information Technology, Physics, or Mathematics

Master’s or PhD preferred for research

Tools

Docker

Kubernetes

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.
2. 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.
3. 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.
4. 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.
5. 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.