Top Artificial Intelligence Jobs in the Philippines

Leads organizational change management and AI enablement across Global Operations. Responsibilities include impact and readiness assessments, stakeholder engagement, communications, training, adoption measurement, AI learning program design, coaching, portfolio governance, prioritization, reporting, and continuous improvement. The role drives adoption of approved AI tools and transformation initiatives while translating complex concepts into practical guidance and measurable business outcomes.

As a Senior Solutions Engineer, you will collaborate with the AI Transformation team to build and maintain AI and automation solutions, integrating diverse internal tools, while modernizing systems to enhance operational efficiency.

Startek provides tech-enabled, human-assisted customer experience (CX) solutions using AI, omnichannel orchestration, and CX analytics. They partner with global brands to analyze customer interactions, deliver CX insights, and drive digital transformation. This posting contains a company overview; specific role responsibilities were not provided.

Own and expand a portfolio of GenAI-native startup accounts. Identify prospects, engage founders and technical leaders, lead technical product discussions, coordinate onboarding and integrations, and develop tailored solutions and proofs of concept. Track usage to uncover expansion opportunities, represent Fireworks in the AI ecosystem, and translate customer feedback into product and engineering improvements. The role requires credible understanding of AI infrastructure, model serving, APIs, inference, tuning, and GPU architecture.

Own AI product builds from concept through production, including requirements gathering, LLM prototyping, data engineering, integrations, evaluation, security, reliability, and cost management. Serve as the primary technical client contact, instrument products with analytics and error analysis, iterate based on usage, and document learnings. The role requires strong engineering fundamentals, AI product delivery experience, data pipelines, web and infrastructure knowledge, and high autonomy.

Own paid acquisition across Google, Bing, Meta, and YouTube, scaling campaigns from creative production through launch, optimization, and attribution. Produce high-volume ad assets using AI tools, manage full-funnel campaigns, improve campaign processes, automate repetitive work, and analyze CAC, ROAS, payback, and product activation data. The role requires hands-on Google Ads expertise, creative production skills, subscription funnel experience, and strong ownership in a remote startup environment.

Develop, deploy, and optimize scalable machine learning solutions, including predictive, supervised, unsupervised, and deep learning models. Build data pipelines, production model-serving infrastructure, monitoring, retraining, and lifecycle processes. Ensure AI solutions meet quality, security, compliance, governance, and responsible AI standards. Research emerging technologies and collaborate with data scientists, engineers, product teams, and business stakeholders to deliver measurable business value.

Leads AI data annotation operations, including workflow management, resource allocation, production tracking, quality assurance, guideline development, audits, and corrective actions. Coaches annotation teams, supports recruitment and onboarding, collaborates with AI engineers and product teams, translates requirements into operational plans, and drives process improvements, automation, tool adoption, and data strategy initiatives.

Designs, implements, and optimizes AI-powered workflow automation solutions. Responsibilities include analyzing business processes, identifying automation opportunities, documenting current and future-state workflows, gathering requirements, integrating automation tools, supporting testing and deployment, monitoring performance, measuring outcomes, troubleshooting issues, and promoting automation best practices. The role collaborates with business and technical stakeholders and supports training, adoption, pilots, and continuous improvement initiatives.

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.

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.

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.

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.

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.

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.