Full-Stack AI Engineer

Description

**Full\-Stack AI Engineer (LLMs, AI Products, Full\-Stack Development)****Full\-Time Remote \| U.S. Business Hours** **About the Role** We’re hiring a highly technical and execution\-focused **Full\-Stack AI Engineer** to build and deploy production\-ready AI\-powered applications. This is not a research\-only AI role. You’ll bridge: * full\-stack software engineering, * AI/ML integration, * scalable infrastructure, * and user\-facing product development to turn AI prototypes into reliable, real\-world applications. You’ll work across: * backend systems, * frontend interfaces, * AI pipelines, * APIs, * vector databases, * and cloud infrastructure to deliver AI products that are scalable, secure, and user\-friendly. If you enjoy: * building AI\-powered SaaS products, * integrating LLMs into production systems, * and owning systems end\-to\-end, this role is a strong fit. **What You’ll Own****AI Model Integration \& LLM Applications*** Deploy and integrate: * + OpenAI models + Hugging Face models + fine\-tuned LLMs + PyTorch / TensorFlow models * Build scalable inference APIs using: * + FastAPI + Flask + Node.js * Develop: * + AI copilots + chatbots + AI assistants + intelligent workflows * Implement: * + embeddings + vector search + RAG pipelines + semantic retrieval systems * Work with: * + Pinecone + Weaviate + FAISS + vector databases * **️ Data Engineering \& AI Pipelines** * Build ETL/ELT pipelines for: * + text data + image data + structured datasets * Automate: * + preprocessing + labeling + transformations + versioning * Orchestrate workflows using: * + Airflow + Prefect + Dagster * Manage datasets inside: * + Snowflake + BigQuery + Redshift **Full\-Stack Application Development*** Build modern front\-end interfaces using: * + React + Next.js + Vue * Develop AI\-powered user experiences including: * + dashboards + assistants + analytics tools + AI workflows * Design backend services and microservices * Connect AI systems with business logic and APIs * Ensure applications are: * + responsive + scalable + secure + production\-ready * **️ Infrastructure, Deployment \& MLOps** * Containerize applications with Docker * Deploy services into Kubernetes environments * Build CI/CD pipelines for: * + application releases + model deployments + infrastructure updates * Monitor: * + latency + cost + uptime + model drift * Use tools such as: * + MLflow + Weights \& Biases + Vertex AI + SageMaker + Kubeflow **Security \& Reliability*** Implement: * + secure APIs + authentication + permissions + access controls + rate limiting * Ensure compliance with: * + GDPR + HIPAA + SOC 2 * Build reliable and fault\-tolerant AI systems **Collaboration \& Product Development*** Work closely with: * + product teams + data scientists + engineering teams * Productionize AI prototypes into scalable systems * Translate product ideas into practical AI\-powered features * Document systems for reproducibility and scalability **✅ Required Experience \& Skills*** 3\+ years experience in: * + software engineering + AI engineering + ML\-integrated systems * Strong Python skills: * + PyTorch + TensorFlow + AI tooling * Strong JavaScript / TypeScript skills: * + React + Node.js + frontend frameworks * Experience deploying AI/ML models into production * Experience with: * + APIs + vector databases + RAG pipelines + embeddings * Strong SQL and cloud data warehouse experience * Experience with Docker and cloud infrastructure * **Nice\-to\-Have Experience** * AI\-powered SaaS product development * LLM fine\-tuning and custom model workflows * MLOps and model lifecycle management * Microservices and serverless architectures * Cost optimization for AI inference workloads * Experience with: * + Vertex AI + SageMaker + Kubeflow + LangChain + AI agents * Startup or high\-growth product experience **What Makes You a Strong Fit*** You can move from prototype production confidently * You understand both software engineering and AI systems deeply * You balance speed, scalability, and reliability * You are highly curious about emerging AI tools * You take ownership and execute independently * You care about real\-world product impact — not just experimentation **What a Typical Day Looks Like*** Improve and deploy AI model APIs * Build frontend experiences for AI\-powered workflows * Optimize vector search and retrieval systems * Maintain AI data pipelines and infrastructure * Monitor model latency, cost, and performance * Collaborate with product teams on AI feature prioritization * Debug production issues and improve reliability * Document systems and deployment workflows **In short:** You transform AI capabilities into scalable, production\-ready applications that solve real business problems. **Key Metrics for Success (KPIs)*** Successful AI feature deployments * Application uptime 99\.9% * Inference latency under target thresholds * Stability and reliability of AI systems * Reduction in manual operational work * User adoption and satisfaction of AI features * Scalability and maintainability of infrastructure **Why This Role Stands Out*** High\-impact AI product engineering role * Opportunity to work on real\-world AI applications * Ownership across the full technical stack * Strong exposure to modern LLM infrastructure and tooling * Fast\-paced engineering environment with meaningful product influence * Opportunity to shape AI architecture from the ground up **Interview Process*** Initial Phone Screen * Video Interview with Pavago Recruiter * Technical Assessment * Client Interview(s) with Engineering Team * Offer \& Background Verification **Apply Now** If you: * love building AI\-powered products, * can own systems end\-to\-end, * understand both full\-stack engineering and applied AI, * and want to ship production\-grade AI experiences, this role is a strong fit for you.