Full-Stack AI Engineer

**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:** 1. Initial Phone Screen 2. Video Interview with Pavago Recruiter 3. Technical Assessment (e.g., deploy a small ML model with API endpoints and basic front-end integration) 4. Client Interview(s) with Engineering Team 5. Offer & Background Verification