LLM Fine-Tuning​/Model Engineer Financial Services

We are looking for an experienced LLM Fine-Tuning & Model Engineer to adapt, evaluate, optimize, and deploy Large Language Models for enterprise AI use cases. The ideal candidate will have strong expertise in model fine-tuning, evaluation, optimization, and deployment of open-weight and custom foundation models using modern ML and GenAI technologies.

• Fine-tune and optimize Large Language Models using domain-specific datasets.
• Design and implement training pipelines utilizing LoRA, QLoRA, PEFT, and Supervised Fine-Tuning (SFT).
• Perform model evaluation, benchmarking, and performance analysis.
• Build and maintain RAG-based AI systems.
• Deploy and monitor models using Azure ML and cloud-native infrastructure.
• Optimize model performance, inference costs, latency, and scalability.
• Conduct experimentation around model distillation and alignment techniques.
• Collaborate with AI Engineers and Data Scientists to improve model quality.
• Ensure model governance, security, and responsible AI standards.

• Advanced Python programming skills.
• Deep understanding of NLP, LLMs, and Transformer architectures.
• Hands-on experience with:
• LoRA / QLoRA
• PEFT
• Supervised Fine-Tuning (SFT)
• Experience with Hugging Face ecosystem.
• Experience deploying open-source models such as Llama, Mistral, Falcon, Qwen, or similar models.
• Experience with Azure Machine Learning and Azure OpenAI.
• Strong understanding of RAG architectures and vector databases.

• Experience with distributed training techniques.
• Knowledge of reinforcement learning from human feedback (RLHF).
• Exposure to Agentic AI frameworks.
• Financial services or banking domain experience.
• MLOps and CI/CD experience.

• Bachelor's or master's degree in computer science, Machine Learning, Artificial Intelligence, or related field.
• 4+ years of Machine Learning or AI engineering experience.
• Proven experience fine-tuning and deploying LLMs in production environments.