Top Machine Learning Jobs in Manila

Supports daily trading operations from execution through settlement, reconciles trading accounts, resolves discrepancies, prepares management reports, and assists with process improvements. The role requires collaboration with traders, stakeholders, and clients; knowledge of financial markets, trading systems, regulations, and compliance; and strong analytical and organizational skills. Preferred capabilities include trading platform experience and applying AI, machine learning, analytics, and workflow automation to improve efficiency and accuracy.

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.

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.

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 technical presales activities for enterprise customers and channel partners across a regional territory. Identifies cybersecurity needs, designs and presents Sophos solutions, conducts demonstrations and proof-of-concepts, enables partners, supports account planning, and drives technical sales closure and revenue. Acts as a trusted cybersecurity advisor across endpoint, network, cloud, XDR, SIEM, and MDR solutions while building customer and partner relationships.

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.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Own and improve the Command|Alert alerting platform, including rule evaluation, anomaly detection, event correlation, reliable Kafka and OpenSearch pipelines, and downstream notifications. Convert security and network telemetry into usable topologies and dependency maps, support LLM-based alert generation, resolve production issues, lead reliability improvements, deliver independent projects, and mentor engineers while shaping technical direction.

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.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

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.