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 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

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.