Full-Stack AI Engineer

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.

Location: Remote

Responsibilities

• 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).
• 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).
• 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.
• 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.
• Ensure AI systems comply with data privacy standards (GDPR, HIPAA, SOC 2).
• Implement rate limiting, access control, and secure API endpoints.
• 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.

Requirements

• 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.