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 OwnAI 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.

📌 Full-Stack AI Engineer (México)
🏢 Pavago
📍 México