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Full-Stack AI Engineer

Full-Stack AI Engineer

PavagoZA
6 days ago
Job type
  • Remote
  • Quick Apply
Job description

Job Title : Full-Stack AI Engineer

Position Type : Full-Time, Remote

Working Hours : U.S. client business hours (with flexibility for model deployments, experimentation cycles, and sprint schedules)

About the Role :

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.

Responsibilities :

AI Model Integration :

  • 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).

Data Engineering & Pipelines :

  • 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).
  • Application Development (Full-Stack) :

  • 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.
  • Infrastructure & Deployment :

  • 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.
  • Security & Compliance :

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

  • 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.
  • What Makes You a Perfect Fit :

  • Strong coder with a foundation in both full-stack development and applied ML / AI.
  • Comfortable building prototypes and scaling them to production-grade systems.
  • Analytical problem solver who balances performance, cost, and usability.
  • Curious and adaptable, staying current with emerging AI / LLM tools and frameworks.
  • Required Experience & Skills (Minimum) :

  • 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.
  • Ideal Experience & Skills :

  • Built and scaled AI-powered SaaS products.
  • Experience with LLM fine-tuning, embeddings, and RAG pipelines.
  • Knowledge of MLOps practices (Kubeflow, MLflow, Vertex AI, SageMaker).
  • Familiarity with microservices, serverless architectures, and cost-optimized inference.
  • What Does a Typical Day Look Like?

    A Full-Stack AI Engineer’s day revolves around connecting models to real-world applications. You will :

  • Review and refine model APIs, testing latency and accuracy.
  • Write front-end code to surface AI features in user-friendly interfaces.
  • Maintain pipelines that clean and prepare new datasets for training or fine-tuning.
  • Deploy updates through CI / CD pipelines, monitoring cost and performance post-release.
  • Collaborate with product and data science teams to prioritize AI features that solve real user problems.
  • Document workflows and results so solutions are repeatable and scalable.
  • In essence : you ensure AI moves from prototype to production — reliable, compliant, and impactful.

    Key Metrics for Success (KPIs) :

  • Successful deployment of AI features to production on schedule.
  • Application uptime ≥ 99.9% and inference latency
  • Reduction in manual workflows replaced by AI features.
  • Model performance tracked and stable (accuracy, drift, false positives / negatives).
  • Positive user adoption and satisfaction of AI-driven features.
  • Interview Process :

  • Initial Phone Screen
  • Video Interview with Pavago Recruiter
  • Technical Assessment (e.g., deploy a small ML model with API endpoints and basic front-end integration)
  • Client Interview(s) with Engineering Team
  • Offer & Background Verification
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