Full-Stack AI Engineer

Full-Stack AI Engineer

Position Type: Full-Time, Remote
Working Hours: U.S. Business Hours
Location: Remote (LATAM, Eastern Europe, Pakistan, India, South Africa Preferred)

About the Role

We are hiring a highly skilled Full-Stack AI Engineer to build, deploy, and scale AI-powered applications that solve real business problems.

This role combines full-stack software engineering with applied AI/ML expertise. You will work across backend systems, AI pipelines, APIs, cloud infrastructure, and frontend applications to bring AI features from prototype to production.

The ideal candidate is both technically strong and product-minded — someone who can move quickly, build scalable systems, and turn modern AI capabilities into reliable, user-friendly products.

You will collaborate closely with engineering, product, and data teams to deliver AI-powered workflows, intelligent automation systems, chat experiences, analytics tools, and scalable machine learning infrastructure.

What You’ll Own

AI & LLM Integration

• Deploy and integrate AI/ML models using OpenAI, Hugging Face, TensorFlow, PyTorch, or similar frameworks
  • Build scalable APIs for AI inference using FastAPI, Flask, or Node.js
  • Develop retrieval-augmented generation (RAG) pipelines using Pinecone, Weaviate, FAISS, or vector databases
  • Implement embeddings, semantic search, and AI-powered workflows
  • Optimize inference performance, latency, and cost efficiency

Full-Stack Application Development

• Build frontend interfaces using React, Next.js, Vue, or modern JavaScript frameworks
  • Develop backend systems and APIs that connect AI models with business logic
  • Create user-facing AI features such as chatbots, copilots, dashboards, and automation tools
  • Ensure applications are responsive, secure, scalable, and production-ready
  • Build microservices and scalable backend architectures

Data Engineering & Pipelines

• Develop ETL pipelines for ingesting, cleaning, transforming, and managing datasets
  • Automate preprocessing, data labeling, and workflow orchestration using Airflow, Prefect, or Dagster
  • Manage structured and unstructured datasets in cloud environments
  • Maintain reliable pipelines for model training, fine-tuning, and evaluation

Infrastructure, DevOps & MLOps

• Containerize AI services using Docker and deploy applications using Kubernetes or cloud infrastructure
  • Build CI/CD pipelines for model deployments and application releases
  • Monitor model performance, drift, costs, and system reliability
  • Work with cloud platforms such as AWS, GCP, Azure, Vertex AI, or SageMaker
  • Improve scalability, uptime, and infrastructure efficiency

Security, Compliance & Reliability

• Implement secure API authentication, access control, and rate limiting
  • Ensure AI systems comply with GDPR, HIPAA, SOC 2, or related compliance requirements
  • Maintain monitoring, logging, and observability for production systems
  • Troubleshoot production incidents and optimize system reliability

Collaboration & Product Development

• Partner with product and data teams to define AI-powered product features
  • Translate AI prototypes into scalable production systems
  • Participate in sprint planning, technical discussions, and architecture decisions
  • Maintain clear technical documentation and reproducible workflows

What Makes You a Great Fit

• You are both a strong software engineer and a hands-on AI builder
  • You enjoy shipping AI-powered features that solve real-world business problems
  • You are comfortable moving from prototype to production independently
  • You think critically about scalability, performance, cost, and usability
  • You stay current with rapidly evolving AI tools, frameworks, and infrastructure
  • You communicate clearly and collaborate effectively across technical and non-technical teams

Required Experience & Skills

• 3+ years of software engineering experience with AI/ML exposure
  • Strong proficiency in Python and JavaScript/TypeScript
  • Experience with AI/ML frameworks such as PyTorch or TensorFlow
  • Experience deploying ML or LLM systems into production environments
  • Strong frontend experience with React, Next.js, or Vue
  • Experience building APIs and backend services
  • Strong SQL skills and experience with cloud data platforms
  • Familiarity with Docker, CI/CD pipelines, and cloud deployments

Preferred Experience

• Experience building AI-powered SaaS platforms or automation products
  • Experience with LLM fine-tuning, embeddings, and RAG systems
  • Familiarity with vector databases and semantic search infrastructure
  • Experience with MLOps tools such as MLflow, Kubeflow, Vertex AI, or SageMaker
  • Knowledge of microservices, serverless architectures, and distributed systems
  • Experience optimizing inference cost and performance at scale

What a Typical Day Looks Like

A Full-Stack AI Engineer’s day revolves around building production-ready AI systems and scalable applications. You will:
• Build and optimize AI-powered APIs and backend services
• Develop frontend interfaces for AI-driven experiences and workflows
• Maintain data pipelines and model integration systems
• Monitor production environments for performance, uptime, and cost efficiency
• Collaborate with engineering and product teams to prioritize and ship AI features
• Troubleshoot system bottlenecks and continuously improve scalability and reliability

In short: you help transform AI capabilities into scalable, production-grade products that drive real business impact.

Key Metrics for Success (KPIs)

• Successful deployment of AI-powered features on schedule
  • Application uptime and infrastructure reliability maintained at high standards
  • Fast and stable inference performance for production endpoints
  • Reduction in manual workflows through AI automation
  • Strong adoption and usage of AI-powered product features
  • Scalable, maintainable, and cost-efficient system architecture

Interview Process

• Initial Phone Screen

• Video Interview with Pavago Recruiter

• Technical Assessment (AI API + Full-Stack Integration Exercise)
  • Client Interview with Engineering Team

• Offer & Onboarding

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