AI Engineer

• Design and build AI-powered web applications, integrate LLMs into the platform
• Develop automation workflows for research and data processing
• Build AI-powered web applications that integrate LLMs and AI models into real product workflows
• Integrate OpenAI, Anthropic, Ollama, or other compatible LLM providers into the platform
• Build dashboards, search capabilities, AI chat functionality, and data pipelines
• Develop automation workflows for research and data processing use cases
• Build automations using platforms such as Make.com, n8n, Zapier, or similar orchestration tools
• Build backend services and APIs using Python with FastAPI or Node.js with Express
• Develop frontend interfaces using React or Next.js
• Act as a technical partner to the client, facilitating brainstorming sessions and guiding non-technical stakeholders toward the best solution

Full-Stack AI Engineer

Computer Vision Engineer

• Review, validate, and enhance training datasets to improve model accuracy and consistency.
• Design, build, and maintain scalable machine learning and data processing workflows.
• Create evaluation methodologies to assess model performance across multiple markets, datasets, and business contexts.
• Monitor model outcomes and recommend enhancements to improve effectiveness and efficiency.
• Run experiments involving feature development, model selection, hyperparameter optimization, and data enrichment techniques.
• Develop AI and computer vision solutions that address complex business requirements.
• Deploy and operationalize machine learning models within production environments.
• Support testing, monitoring, documentation, and continuous improvement of deployed models.
• Manage machine learning workloads and training processes within cloud-based platforms, primarily Azure.
• Utilize SQL to analyze large datasets and generate meaningful insights.
• Work closely with engineering teams to improve automation, reproducibility, and development best practices.

AI/ML Engineer, Computer Vision

• Work closely with AI Ops Managers, client teams, and internal stakeholders to improve content quality and platform readiness.
• Provide practical findings and recommendations to support discovery, launch planning, and client adoption.
• Clearly communicate platform risks, content gaps, performance issues, and improvement opportunities.
• Analyze ticket trends, issue types, and support workflows to help identify viable Care AI use cases.
• Categorize tickets based on automation potential, complexity, and player impact.
• Monitor Care AI performance, including automation rates, escalation trends, unresolved topics, and player friction points.
• Identify recurring failure patterns, content gaps, and opportunities for platform optimization.
• Support the AI Ops Manager in preparing Helpshift Care AI for client onboarding, launch, hypercare, and BAU operations.
• Assist with platform setup, configuration checks, workflow readiness, routing logic, escalation paths, and knowledge source alignment.
• Review FAQs, help center content, macros, ticket examples, and support documentation to assess readiness for Care AI.
• Support Care AI launches by validating readiness, testing workflows, monitoring early performance, and identifying post-launch adjustments.
• Maintain documentation of platform actions, account status, known issues, improvements, and follow-up activities.
• Help build scalable templates, checklists, playbooks, and operating procedures that make Care AI deployments more repeatable.
• Conduct routine quality reviews to ensure Care AI responses are accurate, aligned with client tone, and safe for the player experience.
• Identify risks related to hallucination, poor source content, incorrect routing, unresolved issues, or low-confidence responses.
• Escalate risks quickly and support corrective action plans.
• Support internal enablement efforts by helping teams understand platform requirements and Care AI operating expectations.
• Help build stronger AI fluency across delivery teams through knowledge sharing and continuous improvement.

Platform Lead, Helpshift Care AI

• Build customer-facing and internal product features end to end.
• Develop web flows, backend APIs, admin tools, dashboards and operational systems.
• Support products across insurance, payments, savings, investing, travel and financial services.
• Work with product, design, mobile and operations teams to launch fast.
• Integrate APIs, handle data flows and improve production reliability.
• Use analytics, feedback and production issues to improve features after launch.
• Help build tools that reduce manual work and improve speed across the business.

Full Stack Software Engineer – AI Neobank App

• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems.
• Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows.
• Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence.
• Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production.
• Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust.
• Turn manual processes into reliable AI-enabled systems that can scale.

Applied AI Engineer

• Build AI-powered workflows, assistants, agents and automation systems.
• Apply AI across customer support, CRM, onboarding, claims, renewals, payments, operations and internal tools.
• Work with product and engineering teams to turn manual processes into scalable AI-native systems.
• Build integrations with LLMs, internal data, APIs, documents, knowledge bases and business systems.
• Design evaluation, monitoring and fallback flows so AI outputs are useful, safe and reliable.
• Prototype quickly, test with users or operators, then productionize what works.
• Improve speed, quality and consistency across workflows using AI where it creates real business value.

Applied AI Engineer – AI Neobank App

• Design, train, and iterate on custom object detection models specifically tuned for retail environments, inventory tracking, and product recognition
• Fine-tune and deploy open-source vision-language models (LLaVA, Qwen-VL, InternVL, PaliGemma, etc.) for product understanding, zero-shot classification, and scene reasoning
• Build vision-language-action pipelines that translate visual understanding into downstream decisions
• Take state-of-the-art models and optimize them for edge deployment through quantization, pruning, and architectural optimization
• Build robust data pipelines and annotation workflows to continuously improve model performance on diverse retail scenarios
• Stay ahead of the curve on CV and VLM research, prototype new architectures, and determine what's production-ready
• Mentor engineers, establish best practices for model development, and drive technical decisions around our CV infrastructure

Senior Computer Vision Engineer

Machine Learning Engineer

• Design, develop, and deploy production-ready machine learning models with a strong focus on computer vision applications.
• Build AI capabilities that identify, track, and analyse construction progress from visual data.
• Research, prototype, and implement solutions using LLMs, multimodal AI models, and generative AI technologies.
• Evaluate when to leverage commercial foundation models versus developing custom machine learning solutions.
• Design, build, and maintain scalable ML pipelines covering data collection, labelling, training, evaluation, deployment, and monitoring.
• Source, clean, curate, and prepare high-quality datasets for model training.
• Develop and maintain MLOps infrastructure, including model versioning, CI/CD pipelines, deployment automation, and monitoring.
• Collaborate with Software Engineers, Product Managers, and AI specialists to integrate machine learning capabilities into production applications.
• Optimise model accuracy, inference performance, scalability, and reliability.
• Stay current with emerging machine learning techniques, AI frameworks, and industry best practices.

• Build and ship Android features using Kotlin.
• Create clean mobile flows for insurance, payments, claims, renewals and financial services.
• Work with product and design to simplify complex user journeys.
• Integrate backend APIs and ensure mobile flows are stable, secure and reliable.
• Improve app performance, crash rate, loading states, responsiveness and memory usage.
• Use analytics, user behaviour and production issues to improve the product.
• Build AI-assisted mobile experiences only where they genuinely improve the user journey.

Mobile Developer – AI Neobank App

• Own the technical direction of an agentic AI product being transitioned from an external US vendor to an in-house team
• Lead a small pod, including a developer dedicated to SLM training, setting priorities and reviewing their work
• Design and build using Claude Code, Postgres, and Rust as the core stack
• Establish and enforce governance operations — data handling, model behavior, access controls, and change management
• Embed application security thinking into every layer of the architecture, from code to deployment
• Bring genuine business acumen to technical decisions — balancing customer needs, cost, and delivery timelines
• Manage requirements and delivery through Jira, maintain the codebase in GitHub, and coordinate UI build-out via Loveable
• Produce clear, thorough documentation for architecture, processes, and product decisions
• Act as the primary technical point of contact for leadership and the incoming customer base

Senior Agentic AI Lead

• Lead operational improvement initiatives across the business.
• Design scalable operational frameworks.
• Improve cross-functional collaboration.
• Standardize business processes.
• Increase organizational efficiency.
• Support strategic execution.
• Document workflows.
• Improve SOPs.
• Build operational playbooks.
• Optimize internal processes.
• Improve quality assurance.
• Reduce operational complexity.
• Leverage AI to improve:
• - Documentation
• - Meeting management
• - Workflow design
• - Knowledge management
• - Reporting
• - Process mapping
• - Decision support
• - Operational analytics
• - Communication
• - Continuous improvement
• Collaborate closely with:
• - Executive Leadership
• - HR
• - Product
• - Marketing
• - Sales
• - Partnerships
• - Customer Success
• - Engineering
• - Automation
• - Finance to ensure operational excellence across the organization.
• Monitor KPIs.
• Identify bottlenecks.
• Improve operational visibility.
• Support resource planning.
• Drive accountability.
• Continuously improve business performance.
• Build better systems.
• Improve documentation.
• Increase automation.
• Strengthen AI adoption.
• Reduce manual work.
• Scale operational excellence.

Operations Lead – Digital Systems, AI Operations

Applied AI Engineer – Finance Super App

• Drive HAMERKOP's digital transformation and shape its technology roadmap.
• Identify opportunities for AI, automation, and digital solutions across the organisation.
• Oversee the development and adoption of new tools and products.
• Contribute to hands-on software development, prototyping, workflow automation, and AI-enabled applications.
• Collaborate with consultants, management, clients, and external development partners to design, implement, and improve technology-enabled solutions.

Digital Solutions Lead – AI & Automation

• Research, design, develop, and implement AI and machine learning models to address specific business needs and challenges
• Collect, clean, and preprocess data to make it suitable for AI model training and analysis
• Choose appropriate algorithms and techniques for specific AI tasks, such as classification, regression, or natural language processing
• Train and fine-tune AI models using large datasets to achieve high accuracy and performance
• Conduct rigorous testing and validation of AI models to ensure accuracy and reliability
• Integrate AI solutions into existing software systems or applications, ensuring seamless operation
• Continuously optimize AI algorithms and models for improved performance and efficiency
• Collaborate with cross-functional teams to understand business requirements and translate them into AI solutions
• Maintain comprehensive documentation of AI models, code, and processes for reference and knowledge sharing
• Address and resolve challenges and issues that may arise during AI model development and deployment
• Adhere to ethical AI practices and data privacy guidelines, ensuring responsible and compliant AI development
• Stay updated with the latest AI research and technologies to apply innovative solutions to business problems
• Other duties and projects as assigned.

AI Software Engineer

• Build internal AI agents and automation used in production across our brands.
• Turn manual, repetitive workflows into reliable agents, and own their quality, cost, and reliability.
• Coach non-technical teams to work AI-native so the leverage compounds.
• Help set the standards, stack, and practices the full-time AI team will run on.

Machine Learning Engineer jobs in Philippines

See all jobs on JobTailor