{"id":916791,"url":"https://alion.io/job/pavago-full-stack-ai-engineer-7","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":158,"ghost_share":0,"stale_share":0.994,"repost_share":0,"time_to_fill_p50_days":28,"computed_at":"2026-10-01T05:45:00Z"}},"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":{"label":"US time zones","utc_offset_min":-8,"utc_offset_max":-5},"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["AR","BR","MX","PE"],"hiring_countries_total":4,"salary":null,"salary_estimate":{"min_usd":57000,"max_usd":165000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":585},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Hugging Face","optional":false},{"name":"JavaScript","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Next.js","optional":false},{"name":"Node JS","optional":false},{"name":"OpenAI","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"TypeScript","optional":false},{"name":"Vue.js","optional":false},{"name":"Weaviate","optional":false},{"name":"AI Agents","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Kubeflow","optional":true},{"name":"LangChain","optional":true},{"name":"MLFlow","optional":true},{"name":"Spark","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-09-14T00:00:00Z","employer_posted_date":"2026-09-14","last_verified_at":"2026-10-01T02:04:37Z","board_verified":true,"closed_at":null,"days_open":17,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":17},"description":"Full-Stack AI Engineer (LLMs, AI Products & Full-Stack Development)\nPosition Type: Full-Time, Remote\nWorking Hours: U.S. Business Hours\nAbout the Role\nAt Pavago, one of our clients is hiring a Full-Stack AI Engineer to build and deploy production-ready AI-powered applications.\nThis role combines full-stack software engineering with applied AI to deliver scalable, secure, and user-friendly products. You’ll work across backend systems, frontend applications, AI pipelines, APIs, vector databases, and cloud infrastructure to transform AI prototypes into real-world solutions.\nResponsibilities\nAI & LLM Development\nBuild and deploy AI-powered applications using OpenAI, Hugging Face, PyTorch, TensorFlow, or similar technologies.\nDevelop scalable AI inference APIs with FastAPI, Flask, or Node.js.\nBuild AI assistants, chatbots, copilots, and intelligent workflows.\nImplement embeddings, vector search, RAG pipelines, and semantic retrieval using Pinecone, Weaviate, FAISS, or similar platforms.\nFull-Stack Development\nDevelop frontend applications using React, Next.js, Vue, or similar frameworks.\nBuild backend services, APIs, and microservices that integrate AI with business logic.\nDeliver responsive, scalable, and production-ready AI experiences.\nData Engineering & Infrastructure\nBuild and maintain ETL/ELT pipelines and AI data workflows.\nOrchestrate workflows using Airflow, Prefect, or Dagster.\nManage cloud data platforms such as Snowflake, BigQuery, or Redshift.\nDeploy applications using Docker, Kubernetes, and CI/CD pipelines.\nPerformance & Reliability\nMonitor application performance, inference latency, uptime, and model reliability.\nOptimize AI systems for scalability, cost, and performance.\nImplement secure authentication, permissions, and API protection.\nMaintain compliance with industry security and privacy standards.\nCollaboration\nPartner with product managers, engineers, and data teams to deliver AI-powered features.\nTranslate prototypes into production-ready applications.\nDocument systems and deployment workflows.\nRequired Experience & Skills\n3+ years of software engineering experience with AI/ML integration.\nStrong Python and JavaScript/TypeScript skills.\nExperience with PyTorch, TensorFlow, or similar AI frameworks.\nExperience deploying AI or LLM applications into production.\nStrong frontend experience with React, Next.js, Vue, or similar frameworks.\nExperience with APIs, vector databases, embeddings, and RAG pipelines.\nStrong SQL skills and experience with cloud platforms.\nFamiliarity with Docker, Kubernetes, and CI/CD workflows.\nNice to Have\nExperience building AI-powered SaaS products.\nExperience with LangChain, AI agents, Vertex AI, SageMaker, Kubeflow, or MLflow.\nExperience with LLM fine-tuning and MLOps.\nKnowledge of microservices and serverless architectures.\nStartup or high-growth product experience.\nWhat Success Looks Like\nSuccessful deployment of production AI features.\nReliable, scalable, and secure AI systems.\nHigh application uptime and strong performance.\nEfficient, maintainable infrastructure.\nAI-powered features that deliver measurable business value.\nInterview Process\nInitial Recruiter Screening\nVideo Interview with Pavago Recruiter\nTechnical Assessment\nClient Interview\nOffer & Onboarding\nWhat Happens After You Apply\nRight after you apply, you’ll receive an email invitation from Spark Hire to record your Intro Video. It’s a short, self-recorded video that completes your application and allows hiring managers to get to know you before the interview process begins.\nRather than repeating your background during multiple screening calls, you’ll tell your story once, allowing future interviews to focus on meaningful conversations.\nDon’t overthink it-you can record as many takes as you’d like before submitting. 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