AI Engineer
Develop and refine AI prompts to optimize Large Language Model performance within healthcare EHR systems. Collaborate with cross-functional teams to integrate AI capabilities and ensure alignment with clinical workflows.
Remote AI Engineer Jobs in Philippines
54 AI Engineer jobs in Philippines available for remote work from home. Apply for positions such as AI Engineer, Full-stack AI engineer (LATAM) | Full-Time #PAR1, Backend AI Engineer and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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Develop and refine AI prompts to optimize Large Language Model performance within healthcare EHR systems. Collaborate with cross-functional teams to integrate AI capabilities and ensure alignment with clinical workflows.
You will own a specific lane of the product, building screens and server code while ensuring data isolation between tenants. Additionally, you will review code generated by AI agents and maintain high standards through rigorous testing and pull request reviews.
Design and build the operator control plane for an enterprise AgenticOps platform, connecting real-time web interfaces with multi-agent backends. Develop high-performance visualization tools and low-latency streaming pipelines to enable human-in-the-loop control for autonomous systems.
Design and build the operator control plane for an enterprise AgenticOps platform, connecting real-time web interfaces with asynchronous multi-agent backends. Develop high-performance visual interfaces for network topology and implement low-latency streaming pipelines for real-time agent telemetry.
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Lead a high-performing product team to develop and integrate Cephai-powered AI capabilities into the CINC platform. Collaborate with cross-functional leaders to design production-grade systems that solve client problems while maintaining operational reliability.
Design and develop LLM-powered systems while diagnosing and improving AI performance through rigorous experimentation and evaluation. Provide technical leadership by mentoring engineers and defining architectural standards for production-ready AI applications.
You will design, build, and maintain end-to-end voice agents using LLM orchestration, FastAPI services, and real-time telephony pipelines. You are responsible for the entire agent lifecycle, from initial prompting to managing high-concurrency production calls.
Design, develop, and deploy autonomous AI agents and multi-agent systems to drive intelligent decision-making and workflow automation. Collaborate with cross-functional teams to architect scalable agentic platforms and implement robust monitoring and safety guardrails.
Develop AI agents and automation workflows using LLMs, LangChain, and LangGraph. Implement RAG pipelines and integrate AI solutions with internal systems, APIs, and databases.
You will architect and ship a production-grade conversational nutrition assistant using LangGraph, stateful memory, and multi-step reasoning workflows. You will own the architecture decisions, including the implementation of RAG pipelines, observability frameworks, and middleware orchestration.
You will bridge the gap between functional design and technical implementation by creating low-level designs and prototype code for agentic AI systems. This involves running independent proofs of concept, proving integration patterns, and documenting solutions for a distributed development team.
The role involves leading the development of enterprise Model Context Protocol (MCP) integrations and establishing governed multi-model access. You will be responsible for building reference servers, connecting agentic workflows, and implementing model evaluation and observability frameworks.
The AI Engineer will design, build, and optimize machine learning models and AI solutions to support enterprise automation and intelligent decision-making. They will also collaborate with cloud and engineering teams to deploy models and manage data pipelines.
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Design and maintain production-grade agentic systems, including multi-agent orchestration and RAG pipelines grounded in company data. Implement AI infrastructure on AWS while ensuring compliance, safety, and high-quality output through rigorous evaluation frameworks.
You will build tools to evaluate agent behavior, analyze traces, and implement features for automatic self-improvement. Additionally, you will run experiments on Mastra primitives to optimize configuration and performance.
The role involves developing scalable backend services and integrating Generative AI models, including RAG architectures, into production environments. You will be responsible for writing clean, efficient code, optimizing server performance, and collaborating on technical documentation and code reviews.
Design, develop, and deploy multi-agent AI systems and RAG pipelines for reasoning and task execution. Collaborate with engineering teams to integrate data sources and implement observability frameworks for model performance monitoring.
You will own the end-to-end architecture of AI trading agents, including orchestration, model routing, and the evaluation layer for strategy quality. You will also build data foundations and ensure system observability to maintain high-performance, statistically sound trading strategies.
Design and develop scalable backend services and production-ready Generative AI solutions using LLMs. Collaborate with cross-functional teams to optimize AI workflows and contribute to software architecture and engineering best practices.
You will build and own the backend and Agent workflows for LLM-powered products, including multi-turn conversation systems and RAG. Additionally, you will drive continuous evaluation, ensure production reliability, and shape technical product decisions.
Design, develop, and maintain scalable full-stack web applications using ReactJS and Python/FastAPI. Integrate AI technologies such as LLMs, RAG pipelines, and AI agents into robust backend services.
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You will define the AI technical strategy and roadmap while serving as the internal authority on applied AI system design. You will build and scale reliable GenAI systems, including RAG and agentic architectures, while partnering with cross-functional teams to drive business impact.
You will lead the development of AI-powered applications, setting technical standards for algorithmic quality and scalability. You will also define the AI roadmap, manage deployment, and contribute to prompting standards across the engineering team.
The Applied AI Engineer will build and integrate AI solutions for research, document analysis, and reporting to automate recurring tasks. They will also be responsible for validating AI outputs, maintaining systems, and training team members on new workflows.
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