Top Artificial Intelligence Jobs in Davao City

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.

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 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.

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.

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 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.

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.

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.

Design and develop high-performance backend services for an AI-powered trading assistant: build AI agent execution, trading order execution, AI sub-account and API key lifecycle management, integrate Binance trading APIs, implement skills/tool-calling frameworks, and ensure scalable, low-latency real-time data pipelines, observability, and security.

Responsible for functional and API testing of platform products, designing and executing end-to-end tests across frontend, backend, strategy, and algorithms. Validate personalized user experiences, improve test coverage and QA processes, and drive adoption of AI-assisted test generation and automation. Collaborate with Product, Engineering, Algorithm, and Strategy teams to identify issues and ensure quality.

Early-career DevOps role focusing on Kubernetes and AWS operations, embedding security into the development lifecycle, automating processes, and implementing CI/CD solutions. Remote, fixed-term accelerator program for students and recent graduates.

Use AI-assisted tools like ChatGPT to produce high-volume, conversion-focused, SEO-optimized content (blogs, landing pages, emails). Refine AI drafts, maintain brand voice and factual accuracy, meet ambitious production goals, collaborate with marketing on keyword strategy, and continuously improve AI prompts and workflows to boost engagement and conversions.

Architect and operate sales systems: deploy AI prospecting agents, build automated data-enrichment and scraping pipelines, manage HubSpot CRM architecture and lead routing, run outbound automation, integrate partner workflows, and deliver reporting and SOPs to scale sales operations.

Analyze manual, repetitive processes and build permanent automations using Python, advanced Excel, SQL, and AI/workflow platforms. Create data pipelines, dashboards, and documented solutions that reduce manual effort, improve reporting accuracy, and enable teams to operate without ongoing manual intervention.

The AI-Enhanced Virtual Assistant will support AI integration, administrative tasks, communication management, project coordination, and client interaction to enhance operational efficiency.

Negotiate, redline, and finalize customer and partner contracts (BAAs, DPAs, NDAs, MSAs, SOWs); draft reusable templates; advise on GDPR, CCPA/CPRA and SCCs; ensure contract terms align with SOC 2 and ISO 27001; support Sales and Compliance, accelerate deal velocity with fast redlines and customer calls; build contract processes and workflows at a fast-growing AI startup. Contractor role with Pacific Time availability required.

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.

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.

Produce high-performing AI-driven paid ads from concept to final cut using AI avatars, voiceovers, b-roll, and motion graphics. Match and iterate on existing ad styles, collaborate with copywriters, optimize for hooks/pacing/CTAs, scale volume, and stay current with AI tools to innovate creative formats.