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.

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