CLANX
Senior Applied AI Engineer (Fine Tuning) - Remote
- Compensation
- Not disclosed
- Location
- Remote job
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 3+ years of overall experience as an AI Engineer
- Strong hands-on experience with fine-tuning AI/ML models for production use cases.
- Experience with LLM fine-tuning, including model selection, dataset preparation, training, evaluation, and optimization.
- Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
- Experience deploying and maintaining fine-tuned models in production environments.
- Familiarity with techniques such as LoRA, QLoRA, PEFT, and parameter-efficient fine-tuning.
- Experience working with model evaluation, monitoring, and performance optimization.
- Hands-on experience with AWS, GCP, or Azure cloud platforms.
- Experience with Docker, Kubernetes, CI/CD, and MLOps practices.
- Strong understanding of APIs and distributed system architecture.
- Knowledge of PostgreSQL and modern data infrastructure.
- Experience with RAG systems and vector databases is a strong plus.
- Excellent written and verbal communication skills in English.
Full description
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.
Company Details
Conqr AI is an early-stage startup building AI solutions for regulated, document-heavy professional workflows. The company focuses on privacy, security, reliability, and delivering high-impact AI products for enterprise users.
Website:
Requirements
- 3+ years of overall experience as an AI Engineer
- Strong hands-on experience with fine-tuning AI/ML models for production use cases.
- Experience with LLM fine-tuning, including model selection, dataset preparation, training, evaluation, and optimization.
- Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
- Experience deploying and maintaining fine-tuned models in production environments.
- Familiarity with techniques such as LoRA, QLoRA, PEFT, and parameter-efficient fine-tuning.
- Experience working with model evaluation, monitoring, and performance optimization.
- Hands-on experience with AWS, GCP, or Azure cloud platforms.
- Experience with Docker, Kubernetes, CI/CD, and MLOps practices.
- Strong understanding of APIs and distributed system architecture.
- Knowledge of PostgreSQL and modern data infrastructure.
- Experience with RAG systems and vector databases is a strong plus.
- Excellent written and verbal communication skills in English.
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.