Top Artificial Intelligence Jobs in the Philippines

Design, build, and deploy AI-powered applications and automation using LLMs; integrate HubSpot, EHRs, CRMs, and other business systems via APIs; develop prompts, AI agents, workflows, dashboards, and reports; ensure data quality, security, and documentation; collaborate with cross-functional teams and provide technical training.

Design, build, and deploy AI-powered applications and intelligent workflows using LLMs; integrate HubSpot, CRM, EHR, and third-party systems via APIs; develop and optimize prompts, agents, and automation; create dashboards and reports; ensure data quality, security, and performance; collaborate cross-functionally and provide technical training.

Design scalable, secure automation and AI-enabled solutions (RPA, IDP, integrations, chatbots). Produce build-ready artifacts, define architectures, ensure compliance with data governance and regulatory standards, and support delivery teams to reduce rework and accelerate value realization across transformation initiatives.

The AI Solutions Engineer leads AI development across operations by enhancing existing AI processes, deploying solutions, and coaching non-technical team members.

Design, implement, and monitor AI-driven automations and integrations using platforms like n8n/Make/Zapier. Build LLM-powered workflows (chatbots, transcription pipelines), maintain dashboards and reporting, deploy and tune models, and collaborate with stakeholders to document and optimize end-to-end systems.

Design and deploy AI-driven solutions, build automation workflows, analyze data, support innovation, and collaborate with teams to address business needs.

The Junior Project Manager will lead AI-focused projects, ensuring timely delivery and effective collaboration across teams while managing risks and compliance.

Intern will build log-ingestion pipelines, implement pattern/anomaly detection, prototype agent-assist and post-call summaries, evaluate model metrics, assess data security/PII controls, and present findings to stakeholders while learning AI and support tooling.

Support development of AI-powered tools for internal audit: build ML prototypes (anomaly detection, risk scoring, classification), automate audit workflows, validate models, integrate AI into dashboards, and document technical outputs.

Lead QA automation efforts using Playwright and AI-assisted workflows. Design test strategies, assess risks, create and maintain automated tests, and improve testing efficiency via AI tools. Collaborate across SDLC to ensure reliability, performance, and usability for end-to-end digital solutions.

Lead Android architecture and infrastructure for Binance mobile apps: design, build, optimize, and maintain high-performance Kotlin/Java applications, create shared libraries and toolchains, collaborate on hybrid JavaScript runtimes, and use profiling and AI-assisted debugging to troubleshoot and improve app performance.

Build AI-driven developer tools: coding assistants, knowledge retrieval, workflow automation, and LLM-powered apps for code generation, understanding, and test automation. Collaborate with engineering teams to identify pain points, ship solutions, and experiment with prompt engineering, RAG, and AI agents.

Design, build, and deploy production AI agent workflows and LLM-powered systems. Integrate tools, APIs, databases, and internal services; improve agent quality, latency, safety, and cost. Collaborate with product and operations to translate business problems into practical AI solutions and contribute to backend services and evaluation/guardrail patterns.

Early-career full-time program offering training, mentorship, and hands-on work within Binance's blockchain ecosystem. Participants develop practical skills in Web3, collaborate across teams, and contribute to product and engineering projects while receiving tailored curriculum and 1:1 mentorship.

Lead a Data Analyst team to analyze PB-scale transactional and customer data, build algorithmic systems and visualizations, implement ML/LLM solutions for compliance use cases, support business stakeholders with data requests, and translate findings into actionable recommendations.

Early-career role building AI-powered developer tools and internal LLM applications. Tasks include coding assistants, lightweight RAG pipelines, prompt optimization, testing, documentation, and researching emerging AI technologies to improve engineering productivity.

Early-career software engineer in a fixed-term accelerator contributing to backend/full-stack feature development, testing, CI/CD, and deployments. Collaborate on API and system design, integrate and deploy AI/LLM capabilities (LLM APIs, RAG, embeddings, agents), build datasets/benchmarks, monitor performance, debug issues, and produce technical documentation.

Build internal AI developer productivity tools (coding assistants, retrieval systems, workflow automation). Develop LLM-powered apps for code generation, understanding, and test automation. Integrate AI across the SDLC, embed with teams to remove friction, experiment with prompt engineering, RAG, and AI agents, and evaluate emerging AI technologies.

Design, build, and maintain high-performance backend services and APIs using Java/Spring Boot. Develop and productionize recommendation algorithms and LLM systems, optimize LLM workloads (including on-prem), ensure scalability, reliability, and security, and collaborate across time zones with product and project teams to own end-to-end architecture.

Early-career research role designing and running experiments on LLM reasoning, post-training alignment, and test-time scaling. Implement model variants and training pipelines in PyTorch/Hugging Face, evaluate on crypto-native data, maintain experiment tracking (W&B), and partner with engineering to translate research to production.

Lead AI-driven growth product strategy to improve acquisition, activation, retention, cross-sell, and churn reduction. Design viral loops and referral programs, use data and user insights to inform features, manage roadmaps, run experiments, and collaborate with marketing, operations, and engineering to optimize user funnels and drive scalable organic growth.

Lead end-to-end execution of strategic AI and technology initiatives, driving Agile delivery across engineering, product, data, and infrastructure teams. Remove blockers, manage dependencies, align stakeholders, and accelerate organization-wide AI adoption while fostering a high-ownership, innovation-driven engineering culture.

Six-month early-career internship building LLM-powered agent workflows and production AI components. You'll design prompts, retrieval and tool-use logic, run benchmarks, improve quality/latency/reliability/safety, contribute to backend services/APIs, and ship working prototypes alongside full-time engineers.

Lead planning, design, delivery and promotion of a customer-service chatbot management platform. Use AI and big data to improve CS operations (corpus training, SOPs, assistant replies, summaries). Collaborate with engineering, UX, and CS teams. Monitor KPIs and user cases to drive product improvements.

Design and build internal AI productivity tools and LLM-powered applications to augment developer workflows. Integrate AI into the software development lifecycle, apply techniques like prompt engineering, RAG, and agents, partner with engineering teams to identify bottlenecks, and continuously evaluate emerging AI technologies to improve internal platforms and engineering efficiency.