LLM Engineer

About the role

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. As we continue to grow, we’re looking for a skilled LLM Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology.

Responsibilities

• Design and execute fine-tuning experiments for large language models using supervised, DPO, RLHF, and related techniques
• Lead dataset construction, curation, and quality assurance processes for instruction tuning and preference data
• Build scalable training pipelines on top of modern distributed training frameworks
• Tune hyperparameters, optimizer configurations, and training stability strategies for large-model fine-tuning
• Implement parameter-efficient fine-tuning techniques such as LoRA, QLoRA, and adapter-based methods
• Design rigorous evaluation suites including automated benchmarks, human evaluation, and capability-specific probes
• Implement safety, refusal, and policy evaluations to track model behavior across releases
• Operate large-scale training jobs on GPU clusters, diagnosing failures and recovering training state reliably
• Optimize training throughput using mixed precision, sequence packing, and efficient attention implementations
• Manage model artifacts, lineage tracking, and reproducibility across many concurrent experiments
• Collaborate with product, research, and platform teams to align fine-tuning roadmaps with business needs
• Document training methodology, results, and decisions clearly for technical and non-technical audiences
• Mentor engineers on fine-tuning best practices, evaluation rigor, and responsible deployment
• Stay current with LLM research and translate advances into production-ready fine-tuning recipes

Requirements

• Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent experience
• Six or more years of combined ML research and engineering experience, with significant LLM exposure
• Strong proficiency in Python and modern deep learning frameworks, especially PyTorch
• Hands-on experience fine-tuning transformer-based language models at non-trivial scale
• Familiarity with distributed training strategies including FSDP, ZeRO, and pipeline parallelism
• Experience with RLHF, DPO, or other preference optimization techniques
• Strong understanding of evaluation methodology, benchmarks, and human evaluation design
• Experience operating training jobs on GPU clusters and recovering from failures
• Strong written and verbal communication skills
• Track record of shipping or publishing impactful LLM work

Qualifications

• Required Qualifications
• Preferred Qualifications