Summary: Our client is seeking a Full-Stack AI Engineer to design, build, and deploy scalable, reliable, and user-friendly AI-powered applications, bridging software engineering with applied machine learning. Highlights: 1. Bridge software engineering with applied machine learning 2. Design, build, and deploy AI-powered applications 3. Comfortable building prototypes and scaling them to production systems **Job Title:** Full\-Stack AI Engineer **Position Type:** Full\-Time, Remote **Working Hours:** U.S. client business hours (with flexibility for model 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. This role requires bridging software engineering with applied machine learning, ensuring that models are integrated into production systems that are scalable, reliable, and user\-friendly. The Full\-Stack AI Engineer combines back\-end services, front\-end interfaces, and machine learning pipelines to deliver practical, business\-driven AI solutions. **Responsibilities:** AI Model Integration: * + Deploy pre\-trained and fine\-tuned ML/LLM models (OpenAI, Hugging Face, TensorFlow, PyTorch). + Wrap models in APIs (FastAPI, Flask, Node.js) for scalable inference. + Implement vector search integrations (Pinecone, Weaviate, FAISS) for retrieval\-augmented generation (RAG). Data Engineering \& Pipelines: * + Build ETL pipelines for ingesting, cleaning, and transforming text, image, or structured data. + Automate data labeling, preprocessing, and versioning with Airflow, Prefect, or Dagster. + Store and manage datasets in cloud warehouses (Snowflake, BigQuery, Redshift). Application Development (Full\-Stack): * + Build front\-end UIs in React, Next.js, or Vue to surface AI\-powered features (chatbots, dashboards, analytics). + Design back\-end services and microservices to connect models to business logic. + Ensure responsive, intuitive, and secure interfaces for end users. Infrastructure \& Deployment: * + Containerize ML services with Docker and deploy to Kubernetes clusters. + Automate CI/CD pipelines for model updates and application releases. + Monitor latency, cost, and model drift with MLflow, Weights \& Biases, or custom dashboards. Security \& Compliance: * + Ensure AI systems comply with data privacy standards (GDPR, HIPAA, SOC 2\). + Implement rate limiting, access control, and secure API endpoints. Collaboration \& Iteration: * + Work with data scientists to productionize prototypes. + Partner with product teams to scope AI features aligned with business needs. + Document systems for reproducibility and knowledge transfer. **What Makes You a Perfect Fit:** * Strong coder with a foundation in both full\-stack development and applied ML/AI. * Comfortable building prototypes and scaling them to production\-grade systems. * Analytical problem solver who balances performance, cost, and usability. * Curious and adaptable, staying current with emerging AI/LLM tools and frameworks. **Required Experience \& Skills (Minimum):** * 3\+ years in software engineering with exposure to AI/ML. * Proficiency in Python (PyTorch, TensorFlow) and JavaScript/TypeScript (React, Node.js). * Experience deploying ML models into production systems. * Strong SQL and experience with cloud data warehouses. **Ideal Experience \& Skills:** * Built and scaled AI\-powered SaaS products. * Experience with LLM fine\-tuning, embeddings, and RAG pipelines. * Knowledge of MLOps practices (Kubeflow, MLflow, Vertex AI, SageMaker). * Familiarity with microservices, serverless architectures, and cost\-optimized inference. **What Does a Typical Day Look Like?** A Full\-Stack AI Engineer’s day revolves around connecting models to real\-world applications. You will: * Review and refine model APIs, testing latency and accuracy. * Write front\-end code to surface AI features in user\-friendly interfaces. * Maintain pipelines that clean and prepare new datasets for training or fine\-tuning. * Deploy updates through CI/CD pipelines, monitoring cost and performance post\-release. * Collaborate with product and data science teams to prioritize AI features that solve real user problems. * Document workflows and results so solutions are repeatable and scalable. In essence: you ensure AI moves from prototype to production — reliable, compliant, and impactful. **Key Metrics for Success (KPIs):** * Successful deployment of AI features to production on schedule. * Application uptime 99\.9% and inference latency \< 500ms for key endpoints. * Reduction in manual workflows replaced by AI features. * Model performance tracked and stable (accuracy, drift, false positives/negatives). * Positive user adoption and satisfaction of AI\-driven features. **Interview Process:** * Initial Phone Screen * Video Interview with Pavago Recruiter * Technical Assessment (e.g., deploy a small ML model with API endpoints and basic front\-end integration) * Client Interview(s) with Engineering Team * Offer \& Background Verification