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, AI/ML Integration, Production Deployment, Scalable Systems. Deploy ML/LLM models. Integrate ML/LLM models”
What You'll Achieve.
Ensure systems are scalable; Ensure systems are reliable; Ensure systems are secure; Ensure systems are optimized; Improve automation; Improve user experience; Improve operational efficiency; Build intelligent applications; Own AI systems end-to-end; Ensure applications are production-ready; Support high concurrency; Support scalable AI workloads; Maintainability; Reproducibility; Turn AI capabilities into applications; Successful deployment of AI features; Application uptime >= 99.9%; Inference latency below target; Reliability of AI systems; Scalability of AI systems; Reduction in manual workflows; Stable model performance; Monitoring accuracy; Positive adoption of AI features; Cost optimization improvements
Industry & Context.
Analytical problem solver
What They're Looking For.
Must Have
3+ years of software engineering experience, Python proficiency, JavaScript/TypeScript proficiency, Experience building scalable APIs, Experience building back-end systems, Experience deploying machine learning models into production systems, SQL skills, Experience with cloud data warehouses, Familiarity with Docker, Familiarity with Kubernetes, Familiarity with CI/CD workflows, Experience integrating APIs, Experience integrating vector databases, Experience integrating AI inference services
Nice to Have
Experience building and scaling AI-powered SaaS applications, Hands-on experience with embeddings, Hands-on experience with fine-tuning, Hands-on experience with RAG pipelines, Familiarity with MLOps platforms, Experience with serverless architectures, Experience with microservices, Knowledge of prompt engineering, Knowledge of AI workflow optimization, Experience optimizing inference latency, Experience optimizing AI infrastructure costs, Familiarity with monitoring model drift, Familiarity with evaluation metrics, Familiarity with AI observability practices
What You'll Do.
Integrate ML/LLM models
Implement vector search systems
Monitor model performance
Automate data workflows
Manage datasets and pipelines
Store and manage datasets
Build front-end interfaces
Develop back-end services
Ensure application responsiveness
Ensure application security
Ensure application intuitiveness
Design APIs and services
Containerize services
Build CI/CD pipelines
Monitor infrastructure health
Implement observability
Optimize AI inference performance
Optimize infrastructure costs
Ensure AI system compliance
Implement secure authentication
Implement access controls
Implement rate limiting
Implement API security
Maintain secure data handling
Productionize experimental models
Productionize prototypes
Scope AI-driven features
Prioritize AI-driven features
Contribute to architecture discussions
Contribute to technical planning
Document infrastructure
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; Collaborate across technical teams; Collaborate across non-technical teams
Communication Scope
Communicator capable of collaborating
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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