Overview

Applied AI Engineer with 3+ years of experience in core machine learning, model training, fine-tuning, and production deployment, building scalable AI systems and LLM-powered applications.

Responsibilities

• Own end-to-end delivery of production AI and ML systems from experimentation to deployment.
• Train, fine-tune, and optimize machine learning models, including LLMs and open-weight models.
• Build and maintain training, data processing, and inference pipelines.
• Improve model performance across accuracy, latency, reliability, and cost.
• Implement MLOps best practices for deployment, monitoring, CI/CD, and automated retraining.
• Develop evaluation frameworks, benchmark datasets, and quality checks for production models.
• Design and maintain scalable APIs and services that expose AI capabilities.
• Collaborate with Product, Backend, and Frontend teams to integrate AI into customer-facing workflows.
• Monitor production systems and continuously improve model and infrastructure performance.
• Research and evaluate emerging AI techniques, tools, and frameworks.
• Recruiter Screening
• Hiring Manager Discussion
• Applied AI Technical Assessment
• Founder Round
• Final HR Discussion

Qualifications

• 3+ years of overall experience as an AI Engineer
• Strong hands-on experience with fine-tuning AI/ML models for production use cases.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
• Familiarity with techniques such as LoRA, QLoRA, PEFT, and parameter-efficient fine-tuning.
• Hands-on experience with AWS, GCP, or Azure cloud platforms.
• Strong understanding of APIs and distributed system architecture.
• Knowledge of PostgreSQL and modern data infrastructure.
• Excellent written and verbal communication skills in English.