{"id":135640,"url":"https://alion.io/job/pavago-full-stack-ai-engineer-3","title":"Full-Stack AI Engineer","company":{"id":1117,"name":"Pavago","domain":"pavago.co","url":"https://alion.io/company/pavago","size_band":null,"is_staffing_agency":false,"employer_type":"staffing","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":75,"open_postings":194,"ghost_share":0,"stale_share":0.99,"repost_share":0,"time_to_fill_p50_days":33,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["KE","ZA"],"hiring_countries_total":2,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Datadog","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"GDPR","optional":false},{"name":"Google BigQuery","optional":false},{"name":"HIPAA","optional":false},{"name":"Hugging Face","optional":false},{"name":"JavaScript","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Next.js","optional":false},{"name":"Node JS","optional":false},{"name":"OpenAI","optional":false},{"name":"Pinecone","optional":false},{"name":"Prefect","optional":false},{"name":"Prometheus","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"Snowflake","optional":false},{"name":"SOC 2","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"TypeScript","optional":false},{"name":"Vue.js","optional":false},{"name":"Weaviate","optional":false},{"name":"Weights & Biases","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"Embeddings","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Git","optional":true},{"name":"Kubeflow","optional":true},{"name":"LLM","optional":true},{"name":"Spark","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-07-10T00:00:00Z","employer_posted_date":"2026-07-10","last_verified_at":"2026-10-29T21:51:48Z","board_verified":true,"closed_at":null,"days_open":112,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":111},"description":"Full-Stack AI Engineer - Remote\nAI Engineering | LLMs | Python | React | MLOps | Cloud Infrastructure\nPosition Type: Full-Time, Remote\nWorking Hours: U.S. Client Business Hours (with flexibility for sprint planning, deployments, and experimentation cycles)\nAbout the Role\nAt Pavago, one of our clients is hiring a Full-Stack AI Engineer to design, build, and deploy production-ready AI applications that combine modern software engineering with applied artificial intelligence.\nThis is a highly technical, hands-on role where you’ll build intelligent products from end to end-integrating Large Language Models (LLMs), machine learning models, vector databases, cloud infrastructure, and modern web applications into scalable production systems.\nYou’ll collaborate closely with product managers, data scientists, and engineering teams to develop AI-powered solutions that automate workflows, improve user experiences, and create measurable business impact.\nIf you’re passionate about shipping AI products-not just experimenting with models-this role is built for you.\nWhat You’ll Own\nAI Application Development\nBuild and deploy AI-powered applications using modern software engineering best practices.\nIntegrate LLMs and machine learning models into production environments.\nDevelop intelligent features including:\nAI chatbots\nSemantic search\nDocument intelligence\nAI copilots\nWorkflow automation\nBuild scalable APIs that expose AI capabilities to applications.\nLLMs, RAG & AI Integration\nIntegrate models using:\nOpenAI\nHugging Face\nPyTorch\nTensorFlow\nBuild Retrieval-Augmented Generation (RAG) pipelines.\nImplement semantic search using vector databases including:\nPinecone\nWeaviate\nFAISS\nChromaDB\nOptimize prompt engineering and inference workflows.\nMonitor model accuracy, latency, and production performance.\nData Engineering & AI Pipelines\nBuild ETL pipelines for structured and unstructured data.\nAutomate:\nData ingestion\nCleaning\nValidation\nVersioning\nManage workflows using:\nAirflow\nPrefect\nDagster\nWork with cloud data warehouses including:\nBigQuery\nSnowflake\nAmazon Redshift\nOptimize pipelines for scalability and cost efficiency.\nFull-Stack Development\nBuild modern user interfaces using:\nReact\nNext.js\nVue.js\nDevelop scalable backend services using:\nPython\nFastAPI\nFlask\nNode.js\nBuild APIs that support high-performance AI workloads.\nEnsure applications remain responsive, secure, and production-ready.\nInfrastructure, DevOps & MLOps\nDeploy applications using:\nDocker\nKubernetes\nBuild CI/CD pipelines for both applications and AI models.\nMonitor infrastructure using:\nMLflow\nWeights & Biases\nDatadog\nPrometheus\nImprove:\nInference latency\nInfrastructure reliability\nDeployment automation\nCloud cost optimization\nSecurity & Compliance\nBuild secure AI systems using modern authentication and authorization practices.\nProtect sensitive business and customer data.\nSupport compliance with:\nGDPR\nHIPAA\nSOC 2\nImplement API security, rate limiting, and access controls.\nRequirements\nMust-Have Qualifications\nExperience\n3+ years of software engineering experience with exposure to AI/ML systems.\nExperience building production AI applications.\nExperience deploying machine learning models into production environments.\nCore Skills\nStrong proficiency in:\nPython\nJavaScript / TypeScript\nHands-on experience with:\nOpenAI APIs\nHugging Face\nPyTorch\nTensorFlow\nExperience building scalable REST APIs.\nFront-end development experience with:\nReact\nNext.js\nVue.js\nStrong SQL skills and experience working with cloud databases.\nExperience using:\nDocker\nKubernetes\nCI/CD pipelines\nFamiliarity with vector databases and AI inference services.\nNice to Have\nExperience building AI-powered SaaS platforms.\nExperience with:\nEmbeddings\nFine-tuning\nRetrieval-Augmented Generation (RAG)\nExperience using:\nMLflow\nKubeflow\nVertex AI\nSageMaker\nFamiliarity with:\nServerless architectures\nMicroservices\nExperience optimizing inference latency and cloud infrastructure costs.\nKnowledge of AI observability, evaluation frameworks, and model drift monitoring.\nTools & Technologies\nPython\nJavaScript / TypeScript\nReact\nNext.js\nFastAPI\nFlask\nNode.js\nOpenAI API\nHugging Face\nPyTorch\nTensorFlow\nPinecone\nWeaviate\nChromaDB\nFAISS\nDocker\nKubernetes\nAirflow\nSnowflake\nBigQuery\nRedshift\nMLflow\nGit\nCI/CD\nWhat Makes You a Strong Fit\nPassionate about building production-ready AI products.\nComfortable taking AI solutions from prototype to deployment.\nStrong systems thinker who balances scalability, performance, reliability, and cost.\nOwnership-driven with excellent problem-solving skills.\nCurious about emerging AI frameworks, tools, and best practices.\nStrong collaborator who communicates effectively with both technical and non-technical stakeholders.\nWhat a Typical Day Looks Like\nDevelop APIs that expose AI and LLM capabilities.\nBuild AI-powered front-end experiences.\nOptimize Retrieval-Augmented Generation (RAG) pipelines.\nMaintain ETL workflows for AI inference and training.\nDeploy updates through CI/CD pipelines.\nMonitor production performance and optimize infrastructure.\nTroubleshoot latency, scaling, and model performance issues.\nCollaborate with product and data teams to deliver impactful AI features.\nDocument systems and contribute to long-term platform improvements.\nIn short: You transform cutting-edge AI capabilities into secure, scalable, and reliable production applications that deliver measurable business value.\nKey Metrics for Success (KPIs)\nAI-powered features delivered on schedule.\nApplication uptime ≥ 99.9%.\nAI inference latency consistently meets performance targets.\nHigh reliability and scalability of production AI systems.\nReduced manual workflows through AI automation.\nStable model performance and monitoring accuracy.\nStrong adoption and engagement of AI-powered features.\nContinuous improvement in infrastructure efficiency and cloud cost optimization.\nWhy This Role Stands Out\nEnd-to-end ownership of production AI applications.\nOpportunity to work with modern LLMs, RAG pipelines, and AI infrastructure.\nExposure to cutting-edge AI engineering, MLOps, and cloud technologies.\nCollaborative engineering culture with significant technical ownership.\nFully remote role with long-term career growth.\nClear progression into:\nSenior AI Engineer\nAI Solutions Architect\nStaff Software Engineer\nAI Platform Lead\nEngineering Manager\nInterview Process\nApplication Review\n Spark Hire Intro Video (3-5 minutes)\nRecruiter Interview\nTechnical Assessment (e.g., deploy an AI model with API endpoints and front-end integration)\nFinal Client Interview\nOffer & Background Verification\nWhat Happens After You Apply\nRight after you apply, you’ll receive an email invitation from Spark Hire to record your Intro Video. This short, self-recorded video is the final step that completes your application and can be recorded whenever it’s convenient for you.\nInstead of repeating yourself across multiple screening calls, you’ll introduce yourself once, and your video will be shared with the hiring team. This helps hiring managers evaluate your communication style early, making future interviews more meaningful and reducing unnecessary interview rounds.\nDon’t overthink it-you can record your video as many times as you’d like before submitting it. Only your final submission will be reviewed.\nPlease keep an eye on both your inbox and spam folder for your Spark Hire invitation after submitting your application.\nApply Now\nIf you’re a Full-Stack AI Engineer who enjoys building intelligent applications, deploying production-grade AI systems, and turning cutting-edge technology into real business solutions, we’d love to hear from you. 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