🤖 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:
- 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:
- Manage datasets inside:
- Snowflake
- BigQuery
- Redshift
💻 Full-Stack Application Development
- Build modern front-end interfaces using:
- 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:
- 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.