Full-Stack AI Engineer

🤖 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.