Pavago
AI Engineer
- Compensation
- Not disclosed
- Location
- Pakistan
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Full time
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Proven hands-on experience building and deploying AI or machine learning systems in a professional environment.
- Strong proficiency in Python.
- Experience with machine learning frameworks such as TensorFlow, PyTorch, or equivalent.
- Solid understanding of NLP, large language models (LLMs), or conversational AI, with practical product implementation experience.
- Familiarity with cloud platforms such as AWS, Google Cloud Platform (GCP), or Microsoft Azure.
- Experience with model hosting, deployment, scaling, and monitoring.
- Strong understanding of APIs and integrating AI components into broader software systems.
- Ability to evaluate model performance, troubleshoot issues, and improve system reliability.
- Clear written and verbal English communication skills.
Full description
Position Type: Full-Time | Remote
Working Hours: U.S. Business Hours
About the Role
We’re looking for a skilled AI Engineer to build, deploy, and improve AI-powered systems and features. You’ll work across the full development lifecycle, from prototyping and model development to production deployment, integration, and performance monitoring.
The ideal candidate has strong Python and machine learning skills, practical experience with NLP or LLM-based applications, and the ability to turn AI concepts into reliable, scalable products. You should be comfortable working independently, making technical decisions, and continuously improving deployed systems.
Key Responsibilities
AI Model Development
- Design, build, and iterate on AI-powered systems and features.
- Develop prototypes and turn successful experiments into production-ready solutions.
- Apply machine learning techniques to solve practical product and customer problems.
Pipeline Architecture & Performance
- Design and maintain reliable, scalable data and model inference pipelines.
- Optimize AI systems for performance, reliability, and efficiency.
- Identify and resolve bottlenecks across data processing and model execution.
Integration & Deployment
- Integrate AI models into existing product infrastructure and software systems.
- Manage the process from experimentation and testing through deployment and monitoring.
- Work with APIs and supporting infrastructure to ensure smooth integration.
Applied AI Research & Development
- Stay current with developments in AI, machine learning, NLP, and LLMs.
- Evaluate new techniques and tools for practical use within the existing technical stack.
- Translate relevant research and advancements into measurable product improvements.
Quality, Evaluation & Iteration
- Monitor deployed model performance and diagnose issues.
- Evaluate models against benchmarks and defined quality criteria.
- Drive continuous improvement through systematic testing, evaluation, and retraining where appropriate.
- Document technical approaches, findings, and implementation decisions.
Cross-Functional Collaboration
- Work with the technical team to align AI capabilities with product goals.
- Contribute to architecture decisions and technical roadmaps.
- Communicate progress, technical challenges, and potential blockers proactively.