### Full-Stack AI Engineer – Remote AI Engineering | LLMs | Python | React | MLOps | Cloud Infrastructure Position Type: Full-Time, Remote Working Hours: U.S. Client Business Hours (with flexibility for sprint planning, deployments, and experimentation cycles) ### About the Role At Pavago, one of our clients is hiring a Full-Stack AI Engineer to design, build, and deploy production-ready AI applications that combine modern software engineering with applied artificial intelligence. This is a highly technical, hands-on role where you’ll build intelligent products from end to end—integrating Large Language Models (LLMs), machine learning models, vector databases, cloud infrastructure, and modern web applications into scalable production systems. You’ll collaborate closely with product managers, data scientists, and engineering teams to develop AI-powered solutions that automate workflows, improve user experiences, and create measurable business impact. If you’re passionate about shipping AI products—not just experimenting with models—this role is built for you. ### What You’ll Own ### AI Application Development - Build and deploy AI-powered applications using modern software engineering best practices. - Integrate LLMs and machine learning models into production environments. - Develop intelligent features including: - AI chatbots - Semantic search - Document intelligence - AI copilots - Workflow automation - Build scalable APIs that expose AI capabilities to applications. ### LLMs, RAG & AI Integration - Integrate models using: - OpenAI - Hugging Face - PyTorch - TensorFlow - Build Retrieval-Augmented Generation (RAG) pipelines. - Implement semantic search using vector databases including: - Pinecone - Weaviate - FAISS - ChromaDB - Optimize prompt engineering and inference workflows. - Monitor model accuracy, latency, and production performance. ### Data Engineering & AI Pipelines - Build ETL pipelines for structured and unstructured data. - Automate: - Data ingestion - Cleaning - Validation - Versioning - Manage workflows using: - Airflow - Prefect - Dagster - Work with cloud data warehouses including: - BigQuery - Snowflake - Amazon Redshift - Optimize pipelines for scalability and cost efficiency. ### Full-Stack Development - Build modern user interfaces using: - React - Next.js - Vue.js - Develop scalable backend services using: - Python - FastAPI - Flask - Node.js - Build APIs that support high-performance AI workloads. - Ensure applications remain responsive, secure, and production-ready. ### Infrastructure, DevOps & MLOps - Deploy applications using: - Docker - Kubernetes - Build CI/CD pipelines for both applications and AI models. - Monitor infrastructure using: - MLflow - Weights & Biases - Datadog - Prometheus - Improve: - Inference latency - Infrastructure reliability - Deployment automation - Cloud cost optimization ### Security & Compliance - Build secure AI systems using modern authentication and authorization practices. - Protect sensitive business and customer data. - Support compliance with: - GDPR - HIPAA - SOC 2 - Implement API security, rate limiting, and access controls. ### Requirements ### Must-Have Qualifications ### Experience - 3+ years of software engineering experience with exposure to AI/ML systems. - Experience building production AI applications. - Experience deploying machine learning models into production environments. ### Core Skills - Strong proficiency in: - Python - JavaScript / TypeScript - Hands-on experience with: - OpenAI APIs - Hugging Face - PyTorch - TensorFlow - Experience building scalable REST APIs. - Front-end development experience with: - React - Next.js - Vue.js - Strong SQL skills and experience working with cloud databases. - Experience using: - Docker - Kubernetes - CI/CD pipelines - Familiarity with vector databases and AI inference services. ### Nice to Have - Experience building AI-powered SaaS platforms. - Experience with: - Embeddings - Fine-tuning - Retrieval-Augmented Generation (RAG) - Experience using: - MLflow - Kubeflow - Vertex AI - SageMaker - Familiarity with: - Serverless architectures - Microservices - Experience optimizing inference latency and cloud infrastructure costs. - Knowledge of AI observability, evaluation frameworks, and model drift monitoring. ### Tools & Technologies - Python - JavaScript / TypeScript - React - Next.js - FastAPI - Flask - Node.js - OpenAI API - Hugging Face - PyTorch - TensorFlow - Pinecone - Weaviate - ChromaDB - FAISS - Docker - Kubernetes - Airflow - Snowflake - BigQuery - Redshift - MLflow - Git - CI/CD ### What Makes You a Strong Fit - Passionate about building production-ready AI products. - Comfortable taking AI solutions from prototype to deployment. - Strong systems thinker who balances scalability, performance, reliability, and cost. - Ownership-driven with excellent problem-solving skills. - Curious about emerging AI frameworks, tools, and best practices. - Strong collaborator who communicates effectively with both technical and non-technical stakeholders. ### What a Typical Day Looks Like - Develop APIs that expose AI and LLM capabilities. - Build AI-powered front-end experiences. - Optimize Retrieval-Augmented Generation (RAG) pipelines. - Maintain ETL workflows for AI inference and training. - Deploy updates through CI/CD pipelines. - Monitor production performance and optimize infrastructure. - Troubleshoot latency, scaling, and model performance issues. - Collaborate with product and data teams to deliver impactful AI features. - Document systems and contribute to long-term platform improvements. In short: You transform cutting-edge AI capabilities into secure, scalable, and reliable production applications that deliver measurable business value. ### Key Metrics for Success (KPIs) - AI-powered features delivered on schedule. - Application uptime ≥ 99.9%. - AI inference latency consistently meets performance targets. - High reliability and scalability of production AI systems. - Reduced manual workflows through AI automation. - Stable model performance and monitoring accuracy. - Strong adoption and engagement of AI-powered features. - Continuous improvement in infrastructure efficiency and cloud cost optimization. ### Why This Role Stands Out - End-to-end ownership of production AI applications. - Opportunity to work with modern LLMs, RAG pipelines, and AI infrastructure. - Exposure to cutting-edge AI engineering, MLOps, and cloud technologies. - Collaborative engineering culture with significant technical ownership. - Fully remote role with long-term career growth. - Clear progression into: - Senior AI Engineer - AI Solutions Architect - Staff Software Engineer - AI Platform Lead - Engineering Manager ### Interview Process 1. Application Review 2. Spark Hire Intro Video (3–5 minutes) 3. Recruiter Interview 4. Technical Assessment (e.g., deploy an AI model with API endpoints and front-end integration) 5. Final Client Interview 6. Offer & Background Verification ### What Happens After You Apply Right after you apply, you’ll receive an email invitation from Spark Hire to record your Intro Video. This short, self-recorded video is the final step that completes your application and can be recorded whenever it’s convenient for you. Instead of repeating yourself across multiple screening calls, you’ll introduce yourself once, and your video will be shared with the hiring team. This helps hiring managers evaluate your communication style early, making future interviews more meaningful and reducing unnecessary interview rounds. Don’t overthink it—you can record your video as many times as you’d like before submitting it. Only your final submission will be reviewed. Please keep an eye on both your inbox and spam folder for your Spark Hire invitation after submitting your application. ### Apply Now If you’re a Full-Stack AI Engineer who enjoys building intelligent applications, deploying production-grade AI systems, and turning cutting-edge technology into real business solutions, we’d love to hear from you. Apply today and help shape the next generation of AI-powered products.