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.