Technical skills
About this role
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.