Top Machine Learning Jobs in Quezon City

Develop and evaluate AI prototypes, experiments, and pilots that improve business outcomes in Home Lending. Apply machine learning, deep learning, speech recognition, NLP, and time-series methods; analyze data; communicate recommendations; and help scale validated solutions into production. The role also supports responsible AI governance, stakeholder prioritization, enablement workshops, documentation, and ongoing performance monitoring.

Leads applied AI integration across Home Lending by identifying high-value use cases, running measurable experiments, building prototypes, scaling successful solutions, and developing enablement programs. Partners with engineering, data, platform, governance, and business teams to transition validated concepts into production. Advises stakeholders on AI strategy, responsible adoption, evaluation methods, and impact metrics while coaching teams and coordinating multiple initiatives.

Build and deliver production-grade automation solutions for banking operations using full-stack development, cloud platforms, low-code tools, RPA, AI, and workflow automation. Gather requirements, design and implement solutions, establish CI/CD pipelines, testing, monitoring, and governance, and measure operational benefits. Partner with stakeholders and technology teams, support adoption and training, maintain documentation for audit readiness, and mentor junior engineers.

Lead expense-management tooling enhancements from requirements through implementation and post-launch support. Translate business needs into user stories, acceptance criteria, and process improvements while coordinating stakeholders, dependencies, risks, timelines, testing, training, and adoption. Develop KPIs and dashboards to monitor operational effectiveness, controls, data quality, and compliance. Identify automation opportunities, support governance, and improve workflow efficiency, reporting, user experience, and operational risk management.

Lead payment lifecycle operations, including daily processing, reconciliations, investigations, exception management, and performance tracking. Resolve control and production breaks, manage operational risk, support fraud prevention, and escalate threats or delays. Partner with stakeholders on automation, process improvements, and client experience enhancements while translating business priorities into execution plans and measurable outcomes.

Analyze and improve AI-powered transaction monitoring systems for money laundering, fraud, sanctions, and terrorist financing detection. Review high-risk transactions, identify behavioral patterns, test and refine machine learning models, develop metrics and dashboards, conduct experiments and ad hoc analyses, and provide clear recommendations. Collaborate with compliance, legal, Identity & Trust, and operations teams while ensuring AML and sanctions regulatory adherence and a positive customer experience.

Lead and facilitate Six Sigma improvement projects across multiple business units, apply data-driven statistical problem solving, define performance metrics, present findings to senior leadership, deploy process trainings, manage idea programs, and support operational excellence for 200-400 FTEs.

Designs and deploys predictive models, advanced analytics, and generative AI solutions, including agentic workflows using retrieval-augmented generation and tool calling. Owns end-to-end model delivery, production pipelines, monitoring, evaluation, retraining, and responsible AI practices. Partners with product, engineering, and risk stakeholders to translate business needs into measurable outcomes and builds reusable AI services and libraries.

Leads business process improvement and digital transformation initiatives, including workflow optimization, automation, AI adoption, data analytics, dashboards, and change management. Collaborates with Data Analytics, IT, systems analysts, and process owners to implement measurable efficiency improvements. Applies Lean, Six Sigma, and Kaizen methodologies while supporting Power Platform automation, predictive analytics, intelligent automation, and organizational adoption of new technologies.

Build AI-enabled tools that solve strategic business problems, improve operations through intelligent automation, and accelerate decision-making with machine learning. Design and implement solutions using ML models, large language models, and data science pipelines. Work with cloud technologies, modern development tools, and deep learning frameworks while continuously developing expertise in machine learning.

Leads the strategy, development, and delivery of AI, machine learning, and generative AI solutions across healthcare domains. Responsibilities include designing scalable software, integrating LLMs and agentic AI, maintaining code quality and production operations, collaborating with stakeholders, mentoring engineers, and leading AI/ML teams. The role requires expertise in modern ML frameworks, cloud platforms, DevOps, CI/CD, NLP, model evaluation, prompt engineering, and production-grade AI applications.

Supports APAC middle-office operations for hedge fund clients, including trade capture, affirmations, pricing, settlement, corporate actions, loan lifecycle events, PnL checks, reporting, issue resolution, and vendor coordination. Reviews portfolio pricing against Bloomberg and Reuters, troubleshoots trade and pricing queries, manages escalations, and collaborates with global teams across a follow-the-sun operating model.

Leads global software engineering, quality assurance, DevOps, and deployment teams delivering Guardian software and distributed control system solutions. Defines technology strategy, architecture, roadmaps, and priorities while improving organizational effectiveness, software quality, cybersecurity, automation, and delivery reliability. Partners across functions and geographies, develops engineering talent, guides Agile Release Trains, and drives innovation and continuous improvement for scalable industrial technology products.

Own product initiative delivery from kickoff through release, coordinating Product, Engineering, Data, and Solutions. Track dependencies, risks, blockers, scope changes, timelines, testing, handoffs, and launch readiness. Maintain accurate project status and documentation while ensuring execution remains aligned with product intent. This hands-on role requires technical fluency, cross-functional communication, strong ownership, and daily overlap with an Asia-based technology team.

Analyzes structured and unstructured data, develops and deploys predictive and machine learning models, designs experiments, creates dashboards, ensures data quality, and communicates insights to technical and business stakeholders. Senior-level candidates may lead analytical initiatives, mentor junior staff, and influence strategic decisions.

Own the vision, strategy, and roadmap for a shared lending platform supporting multiple products and markets. Lead platform architecture decisions, real-time credit decisioning, API and configuration design, vendor evaluations, regulatory reporting, data aggregation, and ML-assisted credit capabilities. Translate risk and compliance needs into engineering-ready specifications while coordinating Credit, Risk, Engineering, Design, and country teams. Manage dependencies, delivery sequencing, and the full product lifecycle from research through launch and ongoing iteration.

Analyze catastrophe risk for reinsurance contracts using third-party catastrophe models, validate model outputs, price submissions, and create exposure reports. Apply machine-learning methods to clean and align large datasets and generate decision-ready insights. Develop concise reports, dashboards, and reproducible Python or R notebooks for nontechnical stakeholders. The role requires strong analytical, communication, organizational, and attention-to-detail skills, with flexibility for extended hours during peak periods.

Support SOX testing and risk-based audits across Manulife’s global financial services operations. Perform control walkthroughs, design and operating effectiveness testing, identify root causes, communicate findings, validate issue closures, and apply data analytics to improve audit efficiency. The role requires collaboration across multicultural teams and adherence to internal audit methodology and IIA Standards, with opportunities to use GRC tools, machine learning, and Python.

Build and operate production-grade AI data pipelines and financial knowledge services for equities content. Responsibilities include processing text, tables, documents, and multimedia; creating knowledge bases and search indexes; engineering RAG retrieval and citation systems; integrating LLMs and NLP models; managing orchestration, APIs, quality, cost, observability, reliability, and scalable production operations.

Bridges business needs and AI solutions by gathering requirements, assessing AI and automation opportunities, developing business cases, supporting solution design and delivery, facilitating stakeholder collaboration, assisting testing and implementation, and tracking project outcomes, benefits, reports, and continuous improvement initiatives.

Leads operational oversight for AI-powered products and platforms, monitoring performance, developing dashboards, analyzing business outcomes, and driving process improvement. The role supports AI governance, risk management, compliance, operational standards, forecasting, and scalable operating models. It also partners with engineering, product, and business stakeholders to deliver operational excellence and present strategic recommendations.

Leads business analysis for AI and automation initiatives by identifying adoption opportunities, defining requirements, developing business cases, facilitating stakeholder alignment, and supporting solution design and delivery. Monitors outcomes, value realization, governance, compliance, change management, and continuous improvement while mentoring junior analysts. Translates business objectives into technical requirements and advises executives and technical teams throughout AI transformation programs.

The AI Security Analyst will assess and mitigate risks across AI systems, machine learning platforms, and Generative AI applications. Responsibilities include threat modeling, vulnerability assessment, security monitoring, incident response, root cause analysis, access control, governance, compliance, policy development, and secure AI design reviews. The role partners with engineering, infrastructure, security, governance, and business teams to improve AI security capabilities and promote responsible AI adoption.

Owns AI product backlogs, priorities, user stories, acceptance criteria, and requirements. Translates business needs into technical solutions while coordinating stakeholders, AI engineers, product managers, and delivery teams. Supports Agile planning, product releases, adoption monitoring, continuous improvement, AI innovation, and responsible AI practices. Facilitates workshops, manages expectations, and contributes to AI product strategy and value realization.

Supports enterprise AI governance by developing policies, maintaining control frameworks, assessing AI risks, monitoring regulatory compliance, and reporting governance metrics. Collaborates with legal, compliance, security, AI, and business stakeholders to promote responsible AI practices. The role also researches emerging regulations, supports audits, documents findings, and recommends improvements to governance processes.