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
aimlroles summary, based on the employer's posting.
What you'll do
- Take AI and machine learning systems from experiments through production release
- Fine-tune and improve LLMs and open-weight models, including their accuracy, speed, reliability and cost
- Build training, data preparation and inference pipelines, plus evaluation tests and benchmark datasets
- Put deployment, monitoring, CI/CD and retraining practices in place for production models
- Create scalable APIs for AI features and work with product, backend and frontend teams to integrate them
What you bring
- At least three years working as an AI engineer
- Hands-on experience fine-tuning LLMs for production, from choosing models and preparing data through training and evaluation
- Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow or scikit-learn
- Experience deploying and maintaining tuned models in production, with cloud platforms and MLOps practices
Who this fits
This senior, full-time remote role is based in India. It suits an AI engineer with practical LLM fine-tuning and production deployment experience who can work across model pipelines, APIs and product integration.
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.