Top Artificial Intelligence Jobs in Caloocan

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.

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.

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.

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.

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.

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.

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.

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.

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.

Lead multiple content protection teams to ensure accurate, timely, and compliant processing of copyright infringement and legal claims. Drive SLA, quality, AHT, productivity, compliance, workforce planning, risk management, continuous improvement, automation/AI adoption, and stakeholder collaboration with Legal, WFM, Quality, Training, and HR.

Administer and engineer Salesforce AI platforms, including access controls, permissions, automation, integrations, monitoring, release management, governance, and operational support. Support AI pilots and production rollouts involving AgentForce, case summaries, customer-facing agents, knowledge management, and workflow automation. Collaborate with Customer Success, Product, Security, Support, Services, and TechOps teams to translate requirements into scalable solutions, troubleshoot incidents, improve deployment processes, and maintain secure, reliable platform operations.

Administer Salesforce’s AI ecosystem, including AgentForce, Data Cloud, and Einstein. Manage users, licenses, permissions, security, environments, releases, governance, compliance, monitoring, troubleshooting, documentation, and operational support. Partner with Customer Success, Product, Security, TechOps, and Platform Engineering to enable secure AI pilots, platform rollouts, and customer-facing automation.

Designs, develops, deploys, and operationalizes enterprise AI solutions, including Generative AI and agentic systems. Builds reusable AI services, APIs, SDKs, platforms, and MLOps frameworks; evaluates and optimizes models, prompts, and workflows; integrates AI agents with enterprise applications and business processes; and helps establish AI engineering standards and governance.

Lead and deliver operational excellence and transformation projects for finance functions using OE frameworks (Lean/Six Sigma, DMAIC), process redesign, automation (Alteryx, Power Automate, UiPath), BI (Power BI), stakeholder management, benefits tracking and change management to improve efficiency, accuracy, cost, and customer satisfaction.

Lead PMO activities for Post Trade Solutions, providing portfolio governance, financial oversight, reporting, and delivery assurance. Maintain Clarity PPM data, produce executive governance packs/dashboards, support budgeting/forecasting and month-end processes, manage RAID registers, monitor milestones and dependencies, and drive PMO process improvements. Engage senior stakeholders, ensure compliance with governance standards, and enable decision-making through accurate portfolio insights and financial controls.

The Back-End Developer will design, build, and maintain AI-driven server-side applications, integrating AI/ML models and collaborating with teams to optimize performance and ensure data security.

Design, build, and deploy generative AI agents and reusable components using Microsoft Copilot Studio. Apply prompt engineering, grounding, and LLM techniques; integrate with Microsoft APIs and connectors; validate, test, troubleshoot, and productionize solutions while collaborating with business teams.

Manage AWS services, automate tasks using Python scripts, support AI model training and data handling, and document workflows.

Drive adoption of an AI-powered ecommerce platform by leading full sales cycles, presenting AI solutions and POCs, translating technical concepts into business value, building long-term client relationships, preparing tailored proposals, and coordinating with product and technical teams to ensure successful delivery and adoption.

Lead portfolio financial governance for Post Trade Solutions, owning planning, forecasting, variance analysis, cost allocation, and executive reporting. Act as Clarity PPM SME to ensure data quality, standardise forecasting/reporting, manage risks and milestones, and advise senior stakeholders on commercial and delivery decisions.

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.