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Full-Stack AI EngineerPosition Type: Full-Time, Remote Working Hours: U.S. Business Hours Location: Remote (LATAM, Eastern Europe, Pakistan, India, South Africa Preferred)About the RoleWe are hiring a highly skilled Full-Stack AI Engineer to build, deploy, and scale AI-powered applications that solve real business problems.This role combines full-stack software engineering with applied AI/ML expertise. You will work across backend systems, AI pipelines, APIs, cloud infrastructure, and frontend applications to bring AI features from prototype to production.The ideal candidate is both technically strong and product-minded — someone who can move quickly, build scalable systems, and turn modern AI capabilities into reliable, user-friendly products.You will collaborate closely with engineering, product, and data teams to deliver AI-powered workflows, intelligent automation systems, chat experiences, analytics tools, and scalable machine learning infrastructure.What You’ll OwnAI & LLM IntegrationDeploy and integrate AI/ML models using OpenAI, Hugging Face, TensorFlow, PyTorch, or similar frameworksBuild scalable APIs for AI inference using FastAPI, Flask, or Node.jsDevelop retrieval-augmented generation (RAG) pipelines using Pinecone, Weaviate, FAISS, or vector databasesImplement embeddings, semantic search, and AI-powered workflowsOptimize inference performance, latency, and cost efficiencyFull-Stack Application DevelopmentBuild frontend interfaces using React, Next.js, Vue, or modern JavaScript frameworksDevelop backend systems and APIs that connect AI models with business logicCreate user-facing AI features such as chatbots, copilots, dashboards, and automation toolsEnsure applications are responsive, secure, scalable, and production-readyBuild microservices and scalable backend architecturesData Engineering & PipelinesDevelop ETL pipelines for ingesting, cleaning, transforming, and managing datasetsAutomate preprocessing, data labeling, and workflow orchestration using Airflow, Prefect, or DagsterManage structured and unstructured datasets in cloud environmentsMaintain reliable pipelines for model training, fine-tuning, and evaluationInfrastructure, DevOps & MLOpsContainerize AI services using Docker and deploy applications using Kubernetes or cloud infrastructureBuild CI/CD pipelines for model deployments and application releasesMonitor model performance, drift, costs, and system reliabilityWork with cloud platforms such as AWS, GCP, Azure, Vertex AI, or SageMakerImprove scalability, uptime, and infrastructure efficiencySecurity, Compliance & ReliabilityImplement secure API authentication, access control, and rate limitingEnsure AI systems comply with GDPR, HIPAA, SOC 2, or related compliance requirementsMaintain monitoring, logging, and observability for production systemsTroubleshoot production incidents and optimize system reliabilityCollaboration & Product DevelopmentPartner with product and data teams to define AI-powered product featuresTranslate AI prototypes into scalable production systemsParticipate in sprint planning, technical discussions, and architecture decisionsMaintain clear technical documentation and reproducible workflowsWhat Makes You a Great FitYou are both a strong software engineer and a hands-on AI builderYou enjoy shipping AI-powered features that solve real-world business problemsYou are comfortable moving from prototype to production independentlyYou think critically about scalability, performance, cost, and usabilityYou stay current with rapidly evolving AI tools, frameworks, and infrastructureYou communicate clearly and collaborate effectively across technical and non-technical teamsRequired Experience & Skills3+ years of software engineering experience with AI/ML exposureStrong proficiency in Python and JavaScript/TypeScriptExperience with AI/ML frameworks such as PyTorch or TensorFlowExperience deploying ML or LLM systems into production environmentsStrong frontend experience with React, Next.js, or VueExperience building APIs and backend servicesStrong SQL skills and experience with cloud data platformsFamiliarity with Docker, CI/CD pipelines, and cloud deploymentsPreferred ExperienceExperience building AI-powered SaaS platforms or automation productsExperience with LLM fine-tuning, embeddings, and RAG systemsFamiliarity with vector databases and semantic search infrastructureExperience with MLOps tools such as MLflow, Kubeflow, Vertex AI, or SageMakerKnowledge of microservices, serverless architectures, and distributed systemsExperience optimizing inference cost and performance at scaleWhat a Typical Day Looks LikeA Full-Stack AI Engineer’s day revolves around building production-ready AI systems and scalable applications. You will: • Build and optimize AI-powered APIs and backend services • Develop frontend interfaces for AI-driven experiences and workflows • Maintain data pipelines and model integration systems • Monitor production environments for performance, uptime, and cost efficiency • Collaborate with engineering and product teams to prioritize and ship AI features • Troubleshoot system bottlenecks and continuously improve scalability and reliabilityIn short: you help transform AI capabilities into scalable, production-grade products that drive real business impact.Key Metrics for Success (KPIs)Successful deployment of AI-powered features on scheduleApplication uptime and infrastructure reliability maintained at high standardsFast and stable inference performance for production endpointsReduction in manual workflows through AI automationStrong adoption and usage of AI-powered product featuresScalable, maintainable, and cost-efficient system architectureInterview ProcessInitial Phone ScreenVideo Interview with Pavago RecruiterTechnical Assessment (AI API + Full-Stack Integration Exercise)Client Interview with Engineering TeamOffer & Onboarding
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