Pavago is hiring a 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 deployments, experimentation cycles, and sprint schedules) About the Role Our client is seeking a highly skilled Full-Stack AI Engineer to design, build, and deploy scalable AI-powered applications that solve real-world business problems. This role bridges software engineering with applied machine learning, combining front-end development, back-end systems, AI model integration, and cloud infrastructure into production-ready applications. You will work across the full product lifecycle — from experimentation and prototyping to deployment, optimization, and monitoring. The ideal candidate is both technically strong and execution-focused, capable of building AI-driven systems that are scalable, reliable, performant, and user-friendly. Responsibilities AI Model Integration & LLM Systems • Deploy and integrate pre-trained and fine-tuned ML / LLM models using OpenAI, Hugging Face, TensorFlow, PyTorch, or similar frameworks • Build scalable AI inference APIs using FastAPI, Flask, Node.js, or similar technologies • Implement retrieval-augmented generation (RAG) pipelines using vector databases such as Pinecone, Weaviate, Chroma, or FAISS • Optimize prompt engineering, embeddings, and AI workflows for performance, accuracy, and cost efficiency Full-Stack Application Development • Build responsive front-end applications using React, Next.js, Vue, or similar frameworks • Develop back-end services and APIs connecting AI systems to business workflows and user-facing applications • Design scalable architectures for chatbots, AI assistants, analytics dashboards, search systems, and workflow automation tools • Ensure applications are intuitive, secure, responsive, and production-ready Data Engineering & Pipeline Development • Build ETL/ELT pipelines for ingesting, cleaning, transforming, and processing structured and unstructured datasets • Automate data preprocessing, versioning, labeling, and pipeline orchestration using Airflow, Prefect, Dagster, or similar tools • Store and manage datasets within cloud warehouses such as Snowflake, BigQuery, or Redshift • Maintain reliable data flows supporting training, inference, analytics, and AI operations Infrastructure, Deployment & MLOps • Containerize AI services using Docker and deploy workloads to Kubernetes or cloud-native environments • Build and maintain CI/CD pipelines for AI model updates and application releases • Monitor inference latency, application performance, costs, and model drift using MLflow, Weights & Biases, Prometheus, or custom dashboards • Support scalable and reliable cloud infrastructure on AWS, GCP, or Azure Security & Compliance • Ensure AI systems comply with GDPR, HIPAA, SOC 2, or relevant privacy/security standards • Implement authentication, access control, rate limiting, and secure API practices • Protect user data and AI workflows using modern security standards and best practices Collaboration & Product Development • Collaborate with product managers, designers, and data scientists to prioritize impactful AI features • Translate prototypes into production-grade systems with scalable architecture and maintainable code • Participate in sprint planning, architecture discussions, code reviews, and technical documentation • Maintain clear documentation to support reproducibility, onboarding, and long-term maintainability What Makes You a Perfect Fit • Strong software engineer with deep curiosity around AI/ML systems and emerging technologies • Comfortable moving quickly from prototype to production-grade deployment • Analytical and solutions-oriented with strong debugging and optimization skills • Able to balance performance, scalability, usability, and operational cost • Collaborative communicator who works effectively across technical and non-technical teams Required Experience & Skills • 3+ years of professional software engineering experience with AI/ML exposure • Strong proficiency in Python and JavaScript/TypeScript • Experience with AI/ML frameworks such as PyTorch, TensorFlow, LangChain, or Hugging Face • Experience deploying AI or ML models into production systems • Strong front-end experience with React, Next.js, or Vue • Strong SQL skills and experience with cloud data warehouses • Familiarity with REST APIs, microservices, and distributed systems • Experience with Docker, CI/CD workflows, and cloud infrastructure Preferred Experience & Skills • Experience building and scaling AI-powered SaaS applications • Strong understanding of embeddings, vector databases, and RAG architectures • Experience with LLM fine-tuning, evaluation, and prompt optimization • Familiarity with MLOps tools such as MLflow, Kubeflow, Vertex AI, SageMaker, or Weights & Biases • Experience with serverless architectures and cost-optimized inference systems • Background in SaaS, automation platforms, analytics systems, or AI-driven products What Does a Typical Day Look Like? A Full-Stack AI Engineer’s day revolves around transforming AI capabilities into scalable, production-ready applications. You will: • Review and optimize AI model APIs for latency, accuracy, and reliability • Build front-end interfaces that expose AI-driven functionality to end users • Maintain and improve data pipelines supporting AI systems and analytics • Deploy updates through CI/CD workflows and monitor production performance • Collaborate with product and data science teams on AI feature prioritization • Debug infrastructure, inference, or workflow issues impacting system performance • Document architectures, workflows, and deployment processes for maintainability and scaling In essence: you ensure AI systems move beyond prototypes into secure, scalable, reliable, and impactful production applications. Key Metrics for Success (KPIs) • Successful deployment of AI features aligned with sprint timelines • Application uptime ≥ 99.9% • Inference latency maintained below target thresholds • Reduction in manual workflows through AI automation • Stable model performance and minimized drift or degradation • Positive adoption and engagement with AI-powered features • Scalable, maintainable, and cost-efficient AI infrastructure Interview Process • Initial Phone Screen • Video Interview with Pavago Recruiter • Technical Assessment (e.g., deploy an ML model with API + front-end integration) • Client Interview(s) with Engineering / Product Teams • Offer & Background Verification