Full-Stack AI Engineer

Full-Stack AI Engineer (LLMs, AI Products & Full-Stack Development) Position Type: Full-Time, Remote Working Hours: U.S. Business Hours About the Role At Pavago, one of our clients is hiring a Full-Stack AI Engineer to build and deploy production-ready AI-powered applications. This 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. Responsibilities AI & LLM Development Build and deploy AI-powered applications using OpenAI, Hugging Face, PyTorch, TensorFlow, or similar technologies. Develop scalable AI inference APIs with FastAPI, Flask, or Node.js. Build AI assistants, chatbots, copilots, and intelligent workflows. Implement embeddings, vector search, RAG pipelines, and semantic retrieval using Pinecone, Weaviate, FAISS, or similar platforms. Full-Stack Development Develop frontend applications using React, Next.js, Vue, or similar frameworks. Build backend services, APIs, and microservices that integrate AI with business logic. Deliver responsive, scalable, and production-ready AI experiences. Data Engineering & Infrastructure Build and maintain ETL/ELT pipelines and AI data workflows. Orchestrate workflows using Airflow, Prefect, or Dagster. Manage cloud data platforms such as Snowflake, BigQuery, or Redshift. Deploy applications using Docker, Kubernetes, and CI/CD pipelines. Performance & Reliability Monitor application performance, inference latency, uptime, and model reliability. Optimize AI systems for scalability, cost, and performance. Implement secure authentication, permissions, and API protection. Maintain compliance with industry security and privacy standards. Collaboration Partner with product managers, engineers, and data teams to deliver AI-powered features. Translate prototypes into production-ready applications. Document systems and deployment workflows. Required Experience & Skills 3+ years of software engineering experience with AI/ML integration. Strong Python and JavaScript/TypeScript skills. Experience with PyTorch, TensorFlow, or similar AI frameworks. Experience deploying AI or LLM applications into production. Strong frontend experience with React, Next.js, Vue, or similar frameworks. Experience with APIs, vector databases, embeddings, and RAG pipelines. Strong SQL skills and experience with cloud platforms. Familiarity with Docker, Kubernetes, and CI/CD workflows. Nice to Have Experience building AI-powered SaaS products. Experience with LangChain, AI agents, Vertex AI, SageMaker, Kubeflow, or MLflow. Experience with LLM fine-tuning and MLOps. Knowledge of microservices and serverless architectures. Startup or high-growth product experience. What Success Looks Like Successful deployment of production AI features. Reliable, scalable, and secure AI systems. High application uptime and strong performance. Efficient, maintainable infrastructure. AI-powered features that deliver measurable business value. Interview Process Initial Recruiter Screening Video Interview with Pavago Recruiter Technical Assessment Client Interview Offer & Onboarding What Happens After You Apply Right 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. Rather than repeating your background during multiple screening calls, you’ll tell your story once, allowing future interviews to focus on meaningful conversations. Don’t overthink it—you can record as many takes as you’d like before submitting. Your invitation will come from Spark Hire , so please check both your inbox and spam folder.