Full-Stack AI Engineer
At a glance
Mid-level AI Engineering role at Pavago. Remote, Pakistan.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build AI inference APIs and connect models to application features, business logic and user workflows
- Create frontend and backend features such as chatbots, copilots, dashboards and automation tools
- Prepare data pipelines and deploy AI services on cloud infrastructure, then monitor reliability and cost
- Protect APIs and user access, and document technical designs and architecture decisions
What you bring
- At least three years of software engineering experience, including exposure to AI and machine learning
- Strong Python and JavaScript or TypeScript skills, plus experience with PyTorch or TensorFlow
- You have taken machine learning or LLM solutions into production and built frontend applications with React, Next.js or similar
- Experience with APIs, backend services, SQL data, Docker, CI/CD and cloud platforms
Who this fits
This role suits a mid-level engineer who can work across applied AI, backend systems and frontend applications. It is full-time and remote, with working hours aligned to the U.S. You must be based in one of the preferred regions, including Pakistan.
From the employer
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
At Pavago, one of our clients is hiring a 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’ll work across AI pipelines, backend systems, APIs, cloud infrastructure, and modern frontend frameworks to deliver scalable, production-ready AI applications. You’ll collaborate closely with engineering, product, and data teams to build intelligent automation, chat experiences, analytics tools, and AI-powered workflows.
Responsibilities
AI & LLM Development
- Integrate AI/ML models using OpenAI, Hugging Face, TensorFlow, PyTorch, or similar frameworks.
- Build AI inference APIs using FastAPI, Flask, Node.js, or similar technologies.
- Develop RAG pipelines using vector databases such as Pinecone, Weaviate, or FAISS.
- Implement embeddings, semantic search, and AI-powered workflows.
- Optimize inference performance, scalability, and cost.
Full-Stack Development
- Build frontend applications using React, Next.js, Vue, or similar frameworks.
- Develop backend services and APIs connecting AI models with business logic.
- Build AI-powered features including chatbots, copilots, dashboards, and automation tools.
- Design secure, scalable, and maintainable application architectures.
Data & Infrastructure
- Build ETL pipelines and data workflows using Airflow, Prefect, Dagster, or similar tools.
- Manage structured and unstructured datasets.
- Deploy AI services using Docker, Kubernetes, and cloud platforms such as AWS, Azure, or GCP.
- Build CI/CD pipelines and monitor system performance, reliability, and costs.
Security & Collaboration
- Implement secure authentication, authorization, and API protection.
- Maintain compliance with security and privacy standards where applicable.
- Collaborate with engineering, product, and data teams to deliver production-ready AI solutions.
- Document technical designs, workflows, and architecture decisions.
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 machine learning or LLM solutions into production.
- Strong frontend experience with React, Next.js, Vue, or similar frameworks.
- Experience building APIs, backend services, and SQL-based data solutions.
- Familiarity with Docker, CI/CD pipelines, and cloud platforms.
Nice to Have
- Experience building AI-powered SaaS or automation products.
- Experience with RAG, embeddings, vector databases, and semantic search.
- Familiarity with MLOps tools such as MLflow, Kubeflow, Vertex AI, or SageMaker.
- Experience with microservices, serverless architectures, or distributed systems.
- Experience optimizing AI inference performance and infrastructure costs.
What Success Looks Like
- AI-powered features delivered on schedule.
- Reliable, scalable, and cost-efficient production systems.
- Strong application uptime and inference performance.
- Increased automation through AI-powered workflows.
- Clean, maintainable, and well-documented code.
Interview Process
- Initial Recruiter Screening
- Technical Assessment
- Client Interview
- Offer & Onboarding
What Happens After You Apply
Right after you apply, you’ll receive an email invitation from Spark Hire to record your Intro Video. It’s a short, self-recorded video that completes your application and allows hiring managers to get to know you before the interview process begins.
Rather than repeating your background during multiple screening calls, you’ll tell your story once, allowing future interviews to focus on meaningful conversations.
Don’t overthink it—you can record as many takes as you’d like before submitting. Your invitation will come from Spark Hire, so please check both your inbox and spam folder.