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Pavago

Pavago specializes in connecting businesses with top-tier offshore talent in operations, sales, and marketing, offering a comprehensive recruitment solution designed to reduce cost

Full-Stack AI Engineer

AI EngineerMachine Learning EngineerOtherRemoteMid Level

Location

United States

Posted

94 days ago

Salary

0

Seniority

Mid Level

Job Description

Full-Stack AI Engineer

Pavago

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description 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. Qualifications - 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. 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. 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. Key Metrics for Success (KPIs) - Successful deployment of AI features to production on schedule. - Application uptime ≥ 99.9% and inference latency < 500ms for key endpoints. - 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

Job Requirements

  • 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.
  • 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.
  • Key Metrics for Success (KPIs)
  • Successful deployment of AI features to production on schedule.
  • Application uptime ≥ 99.9% and inference latency < 500ms for key endpoints.
  • 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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