LLM Fine-Tuning / Model Engineer
Role Overview
Adapt, evaluate, optimize, and deploy large language models for enterprise AI use cases.
Work with open-weight and custom foundation models using modern machine-learning and generative-AI technologies.
Key Skills for This Role
Full Job Posting
Role overview
Adapt, evaluate, optimize, and deploy large language models for enterprise AI use cases.
Work with open-weight and custom foundation models using modern machine-learning and generative-AI technologies.
Key responsibilities
- Fine-tune and optimize LLMs using domain-specific datasets.
- Design training pipelines using LoRA, QLoRA, PEFT, and supervised fine-tuning.
- Evaluate and benchmark models and analyze performance.
- Build and maintain RAG-based AI systems.
- Deploy and monitor models with Azure Machine Learning and cloud-native infrastructure.
- Optimize model performance, inference costs, latency, and scalability.
- Experiment with model distillation and alignment techniques.
- Collaborate with AI engineers and data scientists to improve model quality.
- Ensure model governance, security, and responsible AI standards.
Required skills
- Advanced Python and deep understanding of NLP, LLMs, and Transformer architectures.
- Hands-on experience with LoRA or QLoRA, PEFT, supervised fine-tuning, model distillation, and evaluation frameworks.
- Experience with the Hugging Face ecosystem and deploying open-source models such as Llama, Mistral, Falcon, Qwen, or similar.
- Experience with Azure Machine Learning and Azure OpenAI.
- Strong understanding of RAG architectures and vector databases.
Qualifications
- Bachelor’s or master’s degree in computer science, machine learning, artificial intelligence, or a related field.
- 4+ years of machine-learning or AI engineering experience.
- Proven experience fine-tuning and deploying LLMs in production environments.
Preferred skills
- Distributed training, RLHF, Agentic AI frameworks, financial services or banking experience, and MLOps or CI/CD experience are preferred.
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