Full-Stack AI Engineer

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 efficienc… Read the full description on Himalayas.