Senior Applied AI Engineer (Fine Tuning) - Remote
At a glance
Senior AI Engineering role at ClanX. Remote, India · full-time.
Pay not stated Applied AI Engineer salary
remoteroles summary, based on the employer's posting.
What you'll do
- Take AI and machine learning systems from early experiments through production release
- Prepare data, train and tune LLMs and open-weight models, then optimize their production performance
- Build and maintain the data, training and inference workflows that support those models
- Set up model evaluations, benchmark data and quality checks, and improve monitoring and automated retraining
- Create scalable APIs for AI features and work with product and engineering teams to add them to user workflows
What you bring
- At least three years working as an AI engineer
- Practical experience tuning AI or ML models, including LLMs, for production use
- Strong Python skills and experience with frameworks such as PyTorch, TensorFlow or another listed framework
- Experience launching and supporting models in production, with cloud and MLOps tools such as AWS, Docker or another listed tool
Who this fits
This senior remote role suits an AI engineer who has taken model fine-tuning through production and can improve model quality, speed, reliability and cost. You’ll work with product, backend and frontend teams on customer-facing AI workflows. The role is remote in India.
From the employer
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: https://www.conqr.ai/
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.
Research and evaluate emerging AI techniques, tools, and frameworks.
Job Details
Location: Remote
Interview Process
Recruiter Screening
Hiring Manager Discussion
Applied AI Technical Assessment
Founder Round
Final HR Discussion
Important Note
ClanX is a recruitment partner, helping Conqr AI hire an Applied AI Engineer.
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