LLM Fine-Tuning / Model Engineer Financial Services
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 with domain-specific datasets; build training pipelines using LoRA, QLoRA, PEFT, and Supervised Fine-Tuning.
- Evaluate and benchmark models, analyze performance, and experiment with distillation and alignment techniques.
- Build and maintain RAG systems; deploy and monitor models with Azure ML and cloud-native infrastructure.
- Optimize performance, inference costs, latency, and scalability.
- Collaborate with AI Engineers and Data Scientists; support model governance, security, and responsible AI standards.
Required skills
- Advanced Python; strong knowledge of NLP, LLMs, and Transformer architectures.
- Hands-on experience with LoRA or QLoRA, PEFT, Supervised Fine-Tuning, model distillation, and model evaluation frameworks.
- Experience with the Hugging Face ecosystem and 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, reinforcement learning from human feedback, and exposure to Agentic AI frameworks.
- Financial services or banking domain experience; MLOps and CI/CD experience.
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