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 OwnAI & 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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