Pavago
Staffing and Recruiting
Full-StackAIEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Full-Stack AI Engineer at Pavago. Skills: Full-Stack Engineering, Applied Machine Learning, AI Integration, Production Deployment. Deploy and integrate ML/LLM models. Build scalable inference APIs”
What You'll Achieve.
Deployment of AI features on schedule; Application uptime >= 99.9%; Inference latency below target; Reliability of AI systems; Scalability of AI systems; Reduction in manual workflows; Stable model performance; Stable monitoring accuracy; Positive adoption of AI features; Positive usage of AI features; Infrastructure cost optimization; Inference cost optimization
Industry & Context.
Analytical problem solver; Troubleshoot latency; Troubleshoot scaling; Troubleshoot infrastructure
What They're Looking For.
Must Have
3+ years software engineering, Python and JavaScript/TypeScript proficiency, Build scalable APIs, Front-end development experience, Deploy machine learning models, SQL skills, Docker, Kubernetes, CI/CD familiarity, Integrate APIs, vector databases, AI inference
Nice to Have
Build and scale AI SaaS, Embeddings, fine-tuning, RAG pipelines, MLOps platforms familiarity, Serverless architectures experience, Microservices experience, Prompt engineering knowledge, AI workflow optimization knowledge, Optimize inference latency, Optimize AI infrastructure costs, Model drift monitoring, AI observability practices
What You'll Do.
Deploy and integrate ML/LLM models
Build scalable inference APIs
Implement vector search systems
Monitor model accuracy
Monitor model latency
Monitor operational performance
Automate data preprocessing
Automate data labeling
Automate data validation
Automate data versioning
Manage datasets and pipelines
Store datasets in cloud data warehouses
Optimize pipelines for scalability
Optimize pipelines for reliability
Optimize pipelines for cost efficiency
Build front-end interfaces
Develop back-end services
Develop microservices
Ensure applications are responsive
Ensure applications are secure
Ensure applications are intuitive
Ensure applications are production-ready
Design APIs and services
Containerize services using Docker
Deploy workloads to Kubernetes
Build CI/CD pipelines
Maintain CI/CD pipelines
Monitor infrastructure health
Monitor inference latency
Monitor system uptime
Monitor operational costs
Implement observability
Optimize AI inference performance
Optimize infrastructure costs
Ensure AI systems comply with standards
Implement secure authentication
Implement access controls
Implement rate limiting
Implement API security
Maintain secure handling of data
Productionize experimental models
Productionize prototypes
Scope AI-driven features
Prioritize AI-driven features
Contribute to architecture discussions
Contribute to technical planning
Document infrastructure
Develop and refine APIs
Build front-end interfaces
Maintain ETL pipelines
Optimize ETL pipelines
Deploy updates through CI/CD
Monitor production performance
Troubleshoot latency bottlenecks
Troubleshoot scaling bottlenecks
Troubleshoot infrastructure bottlenecks
Collaborate with product teams
Collaborate with data teams
Turn AI capabilities into applications
How You'll Work.
Team & Collaboration
Work closely with product teams; Work closely with engineering teams; Work closely with data teams; Collaborate with data scientists; Partner with product teams; Partner with engineering teams
Full Job Description
### **Job Title: Full-Stack AI Engineer** **Position Type:** Full-Time, Remote **Working Hours:** U.S. client business hours (with flexibility for deployments, experimentation cycles, and sprint schedules) ### **About the Role** Our client is seeking a Full-Stack AI Engineer to design, build, and deploy AI-powered applications that bridge modern software engineering with applied machine learning. This role focuses on taking AI solutions from prototype to production — ensuring systems are scalable, reliable, secure, and optimized for real-world business impact. The ideal candidate combines strong full-stack engineering skills with hands-on experience integrating LLMs, machine learning models, vector databases, and AI workflows into production environments. You will work closely with product, engineering, and data teams to build intelligent applications that improve automation, user experience, and operational efficiency. This is a highly technical, execution-focused role for someone comfortable owning AI systems end-to-end — from infrastructure and APIs to front-end experiences and deployment pipelines. ### **Responsibilities** ### **AI Model Integration & Deployment** • Deploy and integrate pre-trained and fine-tuned ML/LLM models using platforms such as OpenAI, Hugging Face, TensorFlow, and PyTorch • Build scalable inference APIs using FastAPI, Flask, Node.js, or similar frameworks • Implement vector search and retrieval systems using Pinecone, Weaviate, FAISS, or ChromaDB • Design and optimize Retrieval-Augmented Generation (RAG) pipelines for AI-powered applications • Monitor model accuracy, latency, and operational performance in production environments ### **Data Engineering & AI Pipelines** • Build ETL pipelines for ingesting, cleaning, transforming, and processing structured and unstructured datasets • Automate data preprocessing, labeling, validation, and versioning workflows • Manage datasets and pipelines using Airflow, Prefect, Dagster, or similar orchest
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