Pavago
Full-Stack AI Engineer
PavagoArgentina5 days ago
Full-timeRemote FriendlyEngineering, Information Technology
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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