Top Artificial Intelligence Jobs in Davao City

Design and implement complex backend services for Binance Square. Collaborate with product, DevOps, and QA to deliver high-quality microservices, promote engineering best practices, and perform code reviews.

Early-career intern will support AI projects for compliance and risk (KYC, KYB, EDD). Tasks include testing and optimizing LLM/Agent workflows, reviewing prompts and model outputs, documenting findings and edge cases, supporting small-scale implementations, and performing operational analysis to improve processes.

Early-career full stack engineer role to build end-to-end product features, integrate AI coding tools into the workflow, mentor peers, improve cross-team efficiency, and contribute across the product lifecycle from design through deployment.

Build and maintain full-stack web applications using React/Vue frontends and Java/Spring Cloud microservices. Design and consume REST/AI model APIs, work with SQL and NoSQL databases, use AI-assisted development tools, own features end-to-end, collaborate across product and design, ensure high-quality UI/UX, write tests, and contribute to documentation and architecture.

Join as an early-career full stack engineer to build frontend and backend product features, integrate AI coding tools into development workflows, translate product requirements into implementations, and grow toward autonomous full stack delivery while improving cross-team processes.

Support development of internal LLM-based tools (coding assistants, RAG pipelines, prompt optimization), improve engineering efficiency, test and document AI tools, and research emerging AI technologies.

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.

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 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.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

Own post-sale AI customer journey: lead multiple AI implementation projects, manage timelines and risks, coordinate engineering/data teams, drive customer relationships, identify expansion opportunities, and improve delivery processes and playbooks.

Design, develop, fine-tune, deploy, and monitor generative AI and ML models; build data pipelines and integrations; implement automation and agentic systems using tools like LangChain and REST APIs; evaluate emerging AI tools; collaborate with cross-functional teams and mentor junior engineers.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

As a Data Encoder, you'll structure, label, and QA data for training and analytics while meeting quality and throughput targets. This role requires proficiency in AI tools and strong attention to detail.

Lead and execute AI-driven consumer product initiatives for search, recommendations, personalization, and conversational experiences. Collaborate with engineering, design, data, and business teams to define requirements, use data and user feedback to refine products, and deliver improvements within the contract period.

The AI Ops Technical Leader drives the transformation of operations support through hands-on technical delivery and team leadership, focusing on AI and data solutions to improve various operational metrics.

Part-time remote role to configure and optimize GoHighLevel CRM, build automations and AI-driven workflows, manage WordPress site, implement SEO, support paid campaigns, and integrate AI tools (Claude, ChatGPT) to streamline processes and improve lead conversion while working closely with the founder.

Lead a team designing, delivering, and scaling AI and automation solutions across Azure and Microsoft 365. Manage hybrid identity, Azure operations, M365 administration, security/compliance, on-prem servers, vendor relations, and AI strategy (Copilot, OpenAI, Cognitive Services). Oversee governance, POCs, and enterprise adoption while providing L2/3 support and project delivery.

Build and ship production AI systems for a neobank: AI agents, RAG/LLM apps, tool-calling, automations and copilots for onboarding, KYC, support, risk, CRM and operations, while designing for accuracy, cost, latency, security and compliance.

Support Central Operations by evaluating, implementing, and maintaining AI-driven automations and low-code tooling (e.g., Zapier). Build unified dashboards to track AI agent performance, drive internal adoption, document automations, run LLM evaluation/monitoring for human-in-the-loop workflows, and collaborate with product, engineering, and ops to reduce manual work and improve reporting.

Support Central Operations by evaluating, implementing, and maintaining AI agents and low/no-code automations (e.g., Zapier). Build unified dashboards to track AI agent performance, reduce manual work, document automations, run LLM evaluation/monitoring, train stakeholders, and provide feedback to align AI with business workflows.

Lead design and delivery of cloud-native, event-driven platforms and applications. Drive full lifecycle development, champion engineering best practices, implement production-ready solutions, and build AI/LLM capabilities like RAG, embeddings, and model CI/CD across multi-cloud environments.

As a Forward Deployed Engineer, you will collaborate with customers, develop integrations and AI solutions, and optimize data flow within the platform, while also guiding technical adoption and ensuring alignment across teams.

Translate regulatory and policy frameworks into enforceable AI rule logic, deploy LLM-based solutions, onboard and support clients, monitor regulatory changes, conduct targeted legal research, collaborate with product teams, and contribute documentation and performance analysis to improve AI compliance capabilities.