Full-Stack AI Engineer

Job Description

🤖 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.