Top Artificial Intelligence Jobs in Davao City

Oversee and optimize infrastructure supporting AI and machine learning platforms, including servers, cloud environments, compute resources, GPU/CPU allocation, security controls, monitoring, and incident remediation. Design scalable AI environments, automate operations, maintain compliance, and improve system performance and availability. Collaborate with AI engineers, data scientists, and infrastructure teams while mentoring junior administrators and supporting AI platform growth.

Designs, tests, optimizes, and manages prompts for large language models and AI applications. Develops reusable prompt libraries, evaluates AI outputs, conducts experimentation and A/B testing, establishes quality standards, monitors performance, and translates business requirements into scalable prompting strategies. Collaborates with AI engineers, product managers, trainers, and stakeholders while researching generative AI techniques, supporting model evaluation, and documenting best practices.

Leads business analysis for AI and automation initiatives by identifying adoption opportunities, defining requirements, developing business cases, facilitating stakeholder alignment, and supporting solution design and delivery. Monitors outcomes, value realization, governance, compliance, change management, and continuous improvement while mentoring junior analysts. Translates business objectives into technical requirements and advises executives and technical teams throughout AI transformation programs.

The AI Security Analyst will assess and mitigate risks across AI systems, machine learning platforms, and Generative AI applications. Responsibilities include threat modeling, vulnerability assessment, security monitoring, incident response, root cause analysis, access control, governance, compliance, policy development, and secure AI design reviews. The role partners with engineering, infrastructure, security, governance, and business teams to improve AI security capabilities and promote responsible AI adoption.

Owns AI product backlogs, priorities, user stories, acceptance criteria, and requirements. Translates business needs into technical solutions while coordinating stakeholders, AI engineers, product managers, and delivery teams. Supports Agile planning, product releases, adoption monitoring, continuous improvement, AI innovation, and responsible AI practices. Facilitates workshops, manages expectations, and contributes to AI product strategy and value realization.

Design, develop, deploy, and optimize generative AI applications powered by LLMs. Build scalable AI services, APIs, automated workflows, and integrations with enterprise systems and data sources. Evaluate and improve model performance, implement monitoring and quality assurance, and support responsible AI governance, security, and compliance. Collaborate with product, engineering, and business teams while evaluating emerging technologies and contributing to AI experimentation, architecture improvements, documentation, and organizational best practices.

Supports enterprise AI governance by developing policies, maintaining control frameworks, assessing AI risks, monitoring regulatory compliance, and reporting governance metrics. Collaborates with legal, compliance, security, AI, and business stakeholders to promote responsible AI practices. The role also researches emerging regulations, supports audits, documents findings, and recommends improvements to governance processes.

Reviews AI-generated content for accuracy, relevance, safety, bias, compliance, and adherence to quality standards. Documents review outcomes, identifies recurring issues, provides structured feedback to AI trainers and prompt engineers, tracks quality metrics, prepares reports, and supports model improvement, benchmarking, guideline updates, and AI operations best practices.

Leads the architecture, development, deployment, governance, and optimization of enterprise Generative AI solutions. Builds applications using LLMs and foundation models, develops prompt engineering frameworks, improves model performance, and integrates AI into complex business systems. Partners with business and technology leaders on AI strategy, risk management, and roadmap planning. Mentors engineers, evaluates emerging technologies, and leads proofs of concept and innovation initiatives.

Leads the architecture, development, optimization, deployment, and governance of enterprise-scale machine learning systems. Responsibilities include model lifecycle management, MLOps, data quality, feature engineering, monitoring, responsible AI, compliance, technical strategy, innovation initiatives, and mentoring engineering teams.

Manage and optimize AI/ML infrastructure, including cloud environments, GPU/CPU clusters, model deployment pipelines, security configurations, monitoring, troubleshooting, automation, and CI/CD. Develop scripts, maintain documentation, support AI teams, and implement improvements for system reliability, scalability, and performance.

Own the strategy, roadmap, development, delivery, and continuous improvement of AI-powered products. Translate business objectives and customer needs into product requirements, success metrics, and business cases. Partner with engineering, AI, operations, leadership, and customers to manage product lifecycles, adoption, ROI, governance, compliance, and responsible AI practices.

Labels, categorizes, validates, and reviews structured and unstructured data used to train AI and machine learning models. Maintains dataset accuracy, consistency, documentation, and version control; performs quality checks; identifies annotation errors; and collaborates with AI engineers, data scientists, and quality teams. The role also supports annotation guideline development, workflow improvement, calibration exercises, and ongoing dataset maintenance.

Designs, tests, evaluates, and refines prompts for Generative AI systems across customer support, content generation, workflow automation, and knowledge management use cases. The role assesses AI outputs, documents findings, develops reusable prompt libraries, collaborates with AI and product teams, researches emerging techniques, and supports AI testing and implementation.

Leads the strategy, design, implementation, and optimization of AI-driven workflow automation across the organization. Identifies automation opportunities, manages cross-functional initiatives, establishes governance, and measures business outcomes through KPIs and reporting. The role also evaluates emerging AI technologies, supports workflow architecture, develops business cases and ROI assessments, and drives continuous improvement, adoption, and organizational transformation.

Tests and validates AI-powered products, machine learning models, and Generative AI applications. Designs test strategies, executes functional, regression, and acceptance testing, validates model outputs, identifies defects, analyzes quality metrics, documents results, and supports compliance and release readiness. Collaborates with AI engineers, product managers, and business teams while improving AI testing methodologies and quality frameworks.

Develops, optimizes, tests, and documents prompts for conversational AI, automation, and business applications. Evaluates model outputs, conducts prompt experiments, maintains prompt libraries, establishes prompting standards, and translates business needs into AI strategies. Collaborates with AI engineers, trainers, product teams, and stakeholders to support Generative AI deployments, quality assurance, governance, and continuous improvement.

Build, deploy, and monitor full-stack applications, APIs, data pipelines, and AI automations. Develop ReactJS front ends with Python, C#, or .NET Core back ends; manage PostgreSQL and MongoDB databases; create MCP servers and agent tools; implement AI workflows; support production systems; and follow testing, documentation, code review, and agile delivery practices.

Own the end-to-end product experience for agentic AI products, shaping the roadmap, defining features, improving UX, and driving 0-to-1 development. Identify product friction through deep product analysis, usage data, and customer feedback. Lead design reviews, create PRDs, prototypes, onboarding experiences, product marketing content, and in-app education while partnering closely with design and engineering.

Develop user-facing financial AI and equity research algorithms across query understanding, document processing, entity recognition, event detection, retrieval, ranking, and quality evaluation. Build datasets, labeling systems, benchmarks, and production evaluation frameworks. Apply large language models, NLP, machine learning, reinforcement learning, knowledge graphs, and multimodal methods while collaborating with engineers to deploy and continuously optimize financial data and knowledge systems.

Leads analysis, mining, modeling, and visualization of large-scale transactional, operational, and customer data. Partners with compliance teams to prioritize analytical use cases, support regulatory requirements, improve business efficiency, and translate complex findings into actionable recommendations. Oversees multidisciplinary analysts, manages stakeholder data requests, assesses business changes, and develops analytical systems using machine learning, big data technologies, and visualization tools.

Build and implement AI-powered automations across Nucleus Network, focusing initially on CRM, relationship data, enrichment, lead scoring, introductions, and follow-ups. Map business processes, identify automation opportunities, connect platforms through APIs and workflows, develop lightweight tools and dashboards, and maintain reliable systems. The role also requires testing, documentation, stakeholder training, adoption support, and responsible AI practices across Advisory, Ventures, Community, and internal operations.

Join Welo Data’s remote Cebuano contributor network for potential AI projects involving text, image, or audio annotation; search, advertisement, and chatbot evaluation; prompt creation; and cultural or linguistic accuracy review. Projects vary from several days to weeks, with scope, timelines, and compensation provided before participation. Native or near-native Cebuano fluency, English proficiency, digital tool comfort, and strong attention to detail are required.

Architect and improve autonomous AI optimization algorithms for a marketing platform. Analyze A/B tests, integrate AI models, develop multi-touch attribution, expand advertising-channel capabilities, reduce inference costs and latency, incorporate customer feedback, and maintain reliable, continuously improving optimization loops.

Own an AI stockbroking product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, reconciliation, security, scalability, and failure handling. Incorporate KYC, transaction monitoring, limits, privacy, licensing, regulatory approvals, and financial rails. Apply AI with evaluation, safeguards, human oversight, and reliability controls while aligning engineering, compliance, operations, treasury, finance, and design.