Top Artificial Intelligence Jobs in Caloocan

Produce production-ready AI-generated visuals and video for beauty and fashion campaigns, including packaging, product imagery and retail assets. Develop and refine prompts, collaborate with design and packaging teams, maintain prompt libraries and workflows, and ensure outputs meet brand, print and production standards.

Leads Data and AI success management for a commercial business unit, securing stakeholder adoption and connecting analytics initiatives to revenue, retention, profitability, and operational metrics. Directs initiatives from ideation through production and adoption, navigates technical and organizational challenges, and applies SQL, quantitative analytics, data visualization, statistical modeling, data management, warehousing, and data modeling concepts.

Design, build, and deploy production-grade multi-agent systems using Google ADK. Architect deterministic graph workflows, integrate tools and enterprise datastores via MCP, evaluate and debug agent execution, and scale agents on Agent Engine using GCP (Cloud Run/Vertex AI) with a focus on reliability, security, and low latency.

Create high-converting AI-driven video sales letters and AI-UGC for a medical recovery device. Build and manage high-fidelity AI avatars/clones, localize voice/video, rapidly iterate hooks/CTAs for social platforms, and provide design/brand support aligned with medical authority aesthetics.

Drive end-to-end design and development of AI solutions, focusing on performance, reliability, and technical leadership in AI engineering.

Design and deliver production-ready, secure, and scalable AI solutions. Lead discovery workshops, define architectures using LLMs, RAG, vector DBs and AI agents, guide implementation, governance, testing, monitoring, and adoption while partnering with stakeholders to drive measurable business outcomes.

Design, build, and deploy production-grade agentic AI platforms and RAG pipelines, integrate and optimize LLMs, implement cloud-based (Azure primary) scalable AI solutions, apply MLOps, and engage directly with clients to present and deliver measurable business outcomes.

The Data and AI Success Manager will drive the adoption and implementation of Data and AI solutions, guide customers, and collaborate with teams to enhance business value through data-driven insights and strategies while ensuring compliance with data regulations.

This role involves designing high-volume ad creatives for marketing agencies, utilizing AI tools for efficiency, while ensuring brand consistency and effective performance marketing elements.

Rotate across departments to identify and implement AI and automation improvements. Build proofs-of-concept, integrate tools (ChatGPT, Notion AI, n8n, Cursor), automate routine data tasks and dashboards, document improved workflows, report insights, and run internal knowledge-sharing sessions to drive digital transformation.

Design and deploy AI-driven solutions, build automation workflows, analyze data, support innovation, and collaborate with teams to address business needs.

Build and ship AI-powered automation and full-stack features for fulfillment center operations, including dashboards, agentic workflows, multimodal processing, and integrations. Partner with operations to prioritize, deploy, and maintain solutions with observability, guardrails, and adoption support.

Design, document, test, and roll out AI-assisted workflows using Microsoft 365 and Copilot tools. Build SOPs, prompts, and training materials; organize knowledge in SharePoint/Dataverse; collaborate with leadership and external partners; support accounting and project workflows and train staff. Identify automation opportunities and provide ongoing maintenance and improvements.

Operationalize data and AI governance frameworks, implement data quality monitoring, perform root cause analysis, support AI risk/bias assessments, maintain model inventory, advise stakeholders, drive training and continuous improvement for data integrity.

Design, build, and ship AI-powered customer features across backend, frontend, and AI systems. Implement RAG pipelines, multi-agent workflows, integrate LLMs and vector DBs, own APIs/data pipelines, and ensure reliability, performance, and usability while collaborating with product and design.

The Junior Project Manager will lead AI-focused projects, ensuring timely delivery and effective collaboration across teams while managing risks and compliance.

Lead identification, prototyping, and deployment of high-impact AI use cases; integrate AI into systems and workflows; drive adoption via training, playbooks, and change management; establish usage guardrails; partner with IT, Sales Ops, and transformation teams to scale AI capability.

Lead and align Engineering, ML, and Analytics to deliver scalable, reliable production AI and data systems. Own technical direction, architecture, MLOps, and platform reliability while partnering with Product, Sales, and Customers to ensure engineering work maps to measurable business outcomes.

Define and track AI feature success metrics, design and analyze experiments (A/B tests), build AI prototypes, explore data to inform product decisions, create dashboards, improve model interpretability and fairness, and collaborate with product and engineering teams to align models with product goals.

Build end-to-end features across React frontends and Django backends, design data models and APIs, craft responsive UIs with MUI, implement algorithmic/AI logic (scoring, ranking, decision rules), optimize performance and caching, and ship pragmatic production-ready code within a small distributed team.

Design, build, deploy and maintain AI solutions for HR as a spoke of the central AI & Innovation Lab. Translate stakeholder needs into scalable LLM/ML services, ensure data stewardship and compliance, co-develop shared AI platform components, support production systems, and present technical updates to HR leadership.

Lead AI-driven QA strategy and implementation, build AI-enhanced test automation and self-healing frameworks, integrate CI/CD and cloud-native testing, drive agile/DevOps transformation, champion quality culture, and mentor QA teams in AI testing practices.

Lead and coach an Agile squad to deliver production-grade AI solutions. Facilitate Scrum ceremonies, remove impediments, track sprint metrics, ensure backlog readiness, manage risks and dependencies, and coordinate releases while promoting DevOps, automation, and AI-enabled development practices.

Design and prototype AI-powered automations and multi-modal conversational agents, build integrated low-code/code workflows, run POC/MVPs, demonstrate solutions to clients, gather feedback, and collaborate on internal AI innovation.

Design and implement scalable data and AI solutions. Build and optimize EL(T) pipelines, manage data extraction/integration/storage, apply software engineering best practices, mentor team members, collaborate with stakeholders, and drive AI/data platform adoption across projects.