Top Artificial Intelligence Jobs in the Philippines

Build production agentic AI solutions and reusable frameworks on Azure AI Foundry. Responsibilities include multi-agent orchestration, RAG knowledge repositories, reusable tools, Dataverse and Power Platform integration, governance, human approvals, evaluation, observability, security, and infrastructure-as-code deployments. The role requires strong Python and C#/.NET engineering skills, production LLM application experience, Azure expertise, and collaboration with business, technical, and risk stakeholders.

Own end-to-end onboarding for agency and reseller partners onto a white-label Voice AI platform. Responsibilities include discovery, account and branding setup, Voice AI agent deployment, CRM and calendar integrations, automation workflows, phone provisioning, A2P registration, billing configuration, testing, training, documentation, troubleshooting, and launch tracking. The specialist serves as the primary partner contact, monitors onboarding metrics, gathers feedback, and ensures privacy, consent, recording, and TCPA compliance.

Design, build, and maintain Voice AI agents for inbound and outbound calling. Configure conversational flows, prompts, telephony, A2P compliance, carrier reputation, CRM integrations, automations, reporting, and API connections. Join client and sales calls, demonstrate solutions, troubleshoot technical issues, analyze transcripts and call metrics, and optimize agent performance, latency, accuracy, and conversion. Document configurations and collaborate with cross-functional teams on scalable Voice AI implementations.

Own end-to-end video creative, including concepting, scripting, AI generation, editing, publishing, and field production. Build repeatable AI video workflows, develop organic content formats and brand voice, support Meta performance creative, and monitor emerging AI video tools. The role requires exceptional professional editing skills, creative strategy, production coordination, and portfolio-quality work.

Develops and maintains AI, RPA, and automation solutions for risk, control, quality assurance, and audit testing. Responsibilities include analyzing manual processes, building automated workflows and reporting, creating testing scripts, validating results, documenting solutions, integrating tools with IT systems, and training team members. The role also supports risk awareness, stakeholder collaboration, process improvement, and evaluation of emerging automation technologies.

Leads a cross-functional AI transformation pod supporting Finance functions. Identifies and prioritizes AI use cases, oversees sprint-based implementation, drives adoption through coaching and change management, engages senior stakeholders, and scales successful solutions across the CFO organization while delivering measurable improvements in productivity, controls, and decision-making.

Collects, processes, and analyzes large datasets to identify trends, insights, and opportunities that improve business performance. Develops KPI reports, dashboards, data models, algorithms, and visualizations; evaluates data integrity; automates reporting; and communicates findings to technical and non-technical stakeholders. Supports analytics, automation, AI, and machine learning initiatives while collaborating with cross-functional teams and senior leadership.

Design, implement, review, and validate production-grade Rust backend systems for AI training projects. Responsibilities include API development, persistence, asynchronous processing, concurrency, performance optimization, reliability testing, architecture assessment, debugging, and creating reproducible environments with databases, dependencies, fixtures, and containers. The role requires clear technical documentation, evaluation of failure scenarios and race conditions, and consistent delivery of high-quality implementation artifacts.

Develop and support AI applications for knowledge-base ingestion, document parsing, data preprocessing, RAG workflows, and context injection. Build data pipelines for structured and unstructured data, integrate backend services with Python frameworks, and support Docker and Kubernetes deployments. Improve AI accuracy and reduce hallucinations while collaborating with cross-functional teams on testing, deployment, and continuous system improvement.

Build and lead production-grade AI solutions across enterprise systems. Responsibilities include solution architecture, AI-assisted software development, model integration, API and middleware design, cross-business AI application, MLOps collaboration, security-by-design, CI/CD, DevOps, documentation, Agile delivery, and stakeholder communication. The role guides engineers, promotes automation and Lean Six Sigma, and ensures AI solutions are scalable, secure, compliant, and maintainable.

Lead the strategic direction, architecture, governance, and execution of enterprise AI engineering at Axos Bank. Design agentic systems, LLM integrations, MCP servers, AI platforms, quality frameworks, and responsible AI guardrails. Drive AI automation, ROI measurement, executive communication, architecture governance, and cross-functional transformation while mentoring senior AI engineers and establishing organizational engineering standards.

Develop and govern security frameworks for AI/ML platforms, LLM applications, RAG systems, AI agents, and cloud infrastructure. Assess AI-specific threats, secure cloud workloads and APIs, conduct threat modeling and adversarial testing, implement monitoring, and support AI governance, regulatory alignment, security standards, and stakeholder education.

Leads the design, deployment, and adoption of enterprise AI agents and agentic workflows. Architects AI integrations, MCP servers, prompts, plugins, testing strategies, and quality standards. Evaluates model outputs, tracks KPIs and ROI, validates prototypes, identifies automation opportunities, and documents AI practices. Mentors engineers, guides technical decisions, and partners with software teams and stakeholders to deliver scalable, responsible AI solutions.

Build AI-assisted security tools, workflows, and integrations that embed secure engineering guidance across development processes. Analyze designs, code, cloud configurations, and data flows for security risks; maintain traceable security knowledge; automate repetitive activities; document remediation and residual risk; and collaborate with architects and engineering teams to turn prototypes into maintainable capabilities.

Design and lead enterprise security architecture reviews across applications, APIs, cloud platforms, and integrations. Build AI-assisted security solutions, automation, APIs, reusable frameworks, and developer tooling integrated with Git workflows, CI/CD pipelines, and infrastructure-as-code. Enable secure software development through threat modeling, security requirements, design patterns, DevSecOps practices, and cloud security guidance. Partner with engineering, platform, product, and business stakeholders to manage risks, establish guardrails, and scale secure engineering capabilities.

Designs and executes quality strategies for AI agentic applications across development and production. Responsibilities include automated testing of LLM accuracy, RAG ingestion and attribution, agent planning, tool use, safety, security, recovery behavior, and distributed workflows. The role establishes evaluation metrics and quality gates, builds continuous regression suites, analyzes production failures, and creates auditable tests. Requires 5+ years of QA experience, automation expertise, programming skills, and hands-on GenAI or agentic AI testing experience.

Build AI agents, scripts, and full-stack tools that automate media production workflows. Develop frontend, backend, APIs, and data systems; integrate generative video, image, and voice models; automate rendering, editing, publishing, and device operations; and continuously measure and improve productivity for creative teams.

Design and deliver enterprise AI solutions, including generative AI applications, agentic workflows, RAG systems, LLMOps capabilities, predictive models, and full-stack cloud services. Build scalable APIs, microservices, data platforms, and production-grade engineering infrastructure. Partner with product, business, technology, and control teams to translate investment and business challenges into responsible, measurable AI outcomes. Apply financial markets and asset management knowledge to relevant analytics and solutions.

Lead the architecture and delivery of enterprise AI and agentic systems, including LLM applications, RAG solutions, AI platforms, predictive models, and full-stack services. Establish LLMOps, cloud-native engineering, observability, testing, and infrastructure standards. Translate financial and investment challenges into scalable production solutions, guide prototypes through enterprise adoption, mentor engineers, and partner with business, product, technology, and control stakeholders.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Own full-cycle recruiting for Sales, Marketing, Partnerships, Solutions Engineering, and Customer Success. Partner with GTM leaders and executives on hiring strategy, candidate profiles, talent pipelines, market mapping, and compensation alignment. Source and engage high-caliber talent through outreach, networking, referrals, and AI-powered tools. Help optimize recruiting operations, tooling, analytics, and scalable, inclusive hiring processes.

Reviews and creates AI-generated images, videos, voices, scripts, captions, and product content for accuracy, realism, quality, and brand consistency. Tracks content through production, revisions, approval, publication, and client delivery while documenting issues and coordinating with cross-functional teams to meet deadlines and quality standards.

Build and scale customer-facing and internal web applications for an AI-powered eCommerce customer service platform. Develop and enhance AI agents, integrate eCommerce platforms, apply privacy and security practices, and help shape the product’s technical direction. As the founding technology team member, the role offers significant ownership, equity, and potential progression into technical leadership.

Design, deploy, and optimize multi-agent conversational AI workflows using GECX, CX Agent Studio, and custom Python or TypeScript Code Agents. Build backend tools, callbacks, integrations with databases, catalogs, CRMs, and CCaaS platforms, and create generative playbooks and routing logic. Use CLI-based development, linting, testing, simulation evaluations, and CI/CD workflows to ensure safe, reliable, low-latency voice and chat agents.

Lead governance and responsible deployment for Salesforce Agentforce and Einstein capabilities. Define AI policies, risk assessments, guardrails, approval standards, monitoring, audit processes, and trust metrics. Advise teams on agent design, grounding, prompts, hallucination prevention, human handoffs, and production readiness. Partner with data, security, legal, compliance, and architecture teams to ensure privacy, regulatory compliance, data quality, and enterprise risk controls. Develop reusable patterns, training, and playbooks while driving continuous improvement across global business groups.