Top Artificial Intelligence Jobs in Caloocan

Lead end-to-end delivery and operationalization of AI capabilities, from business need and pilot through evidence-based governance, approval, rollout, hypercare, and operational handoff. Manage gate reviews, executive decision materials, steering committees, action tracking, risk escalation, and ownership transitions. The role requires strong project or program management experience, AI or automation delivery fluency, governance expertise, excellent written English, and availability for a long-term remote night shift.

As a Senior AI Engineer, you'll design and deliver scalable AI solutions, lead LLM integration, and mentor engineers while ensuring performance, reliability, and quality compliance.

Lead enterprise AI programs from concept through production deployment. Define roadmaps, milestones, success measures, and use-case priorities while coordinating business, product, engineering, data, and platform teams. Manage risks, dependencies, governance, monitoring, and operational excellence for AI and Agentic AI solutions. Provide executive reporting, support organizational adoption, and ensure adherence to Responsible AI, security, and data privacy standards.

Develops and supports SAP BI and data integration solutions, including data acquisition, cleansing, ETL, modeling, testing, troubleshooting, reporting, and documentation. Supports global analytics projects, business reporting, incidents, and on-call operations. Evaluates data sources, designs relational and dimensional models, automates repetitive tasks, and collaborates with analysts, engineers, and international business teams.

Support ML engineers operating production training and inference workloads across Kubernetes, cloud, and GPU infrastructure. Diagnose distributed PyTorch, CUDA, NCCL, networking, storage, scheduling, and performance issues; analyze observability data; advise customers during incidents; and improve reliability through automation, tooling, documentation, runbooks, and operational processes. The role is remote in the Philippines or Singapore and follows a Sunday-Wednesday APAC shift.

The position offers IT technical support, focusing on aiding users with technology-related issues and ensuring seamless business operations within the company.

Lead and scale a Manila-based, client-facing Data, Analytics, and AI practice across the deal lifecycle. Deliver AI/ML/GenAI solutions, manage mid-to-large analytics engagements, drive go-to-market and pre-sales, mentor junior staff, and ensure high-quality end-to-end delivery for M&A and transaction advisory work.

Build and maintain AI-powered business workflow automations using Make.com, Power Automate, and related platforms. Administer Zendesk workflows and AI customer-service systems, create internal AI assistants and Microsoft 365 productivity automations, document processes, test for errors and hallucinations, and coordinate light Amazon and Walmart marketplace tasks. The role requires strong process thinking, troubleshooting, documentation, and practical experience integrating AI tools, ticketing systems, APIs, and business applications.

Lead organizational AI adoption by identifying automation opportunities, designing AI-powered workflows and agents, engineering prompts, integrating AI with business systems, and building knowledge assistants using RAG. Evaluate emerging AI tools, configure automation platforms, monitor quality and governance, document solutions, and train internal teams. The role requires strong LLM, prompt engineering, workflow automation, API integration, analytical, communication, and project-management skills.

Own B2B SaaS and AI products end to end, from customer problem discovery through specifications, prototyping, QA, launch, and measurable outcomes. Collaborate with engineering, design, and QA; interpret APIs and integrations; gather customer feedback; prioritize initiatives; and improve adoption, revenue, retention, and efficiency. The role requires strong technical fluency, customer engagement, decision-making, and the ability to operate independently in a fast-paced startup environment.

Manage post-sales success for B2B SaaS customers by configuring software, implementing AI agents and workflows, troubleshooting technical issues, developing prompts and integrations, and coordinating with engineering. The role owns customer issues, provides training and guidance, documents solutions, and builds strong client relationships. Candidates need customer success experience, technical aptitude, communication and problem-solving skills, organizational ability, and familiarity with AI, APIs, integrations, CRM, or technical support tools.

Own the end-to-end development and delivery of B2B SaaS and AI features. Prototype concepts, write precise engineering specifications, manage APIs and integrations, test releases, gather customer feedback, troubleshoot bugs, prioritize the backlog, and verify customer outcomes. The role requires hands-on technical product management, decisive prioritization, strong documentation, and close collaboration with engineering and customers.

Sales Engineer supporting commercial efforts by combining technical expertise in industrial automation, test & measurement, digital twin, AI, and industrial cybersecurity to engage customers, demonstrate solutions, and enable sales across energy transition and related industries.

The Technical Support Specialist L2 handles escalated technical issues, implements automation scripts, and collaborates with a network analyst for effective IT support.

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.

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.

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.

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.