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...

Summary and match are AI-generated; verify details on the original posting.

Listed via Himalayas. All sources