Job Title: Applied Scientist / ML Engineer

We are looking for a hands-on Applied Scientist / ML Engineer to support the development and improvement of AI/ML solutions, with a strong focus on Large Language Models, model accuracy, evaluation frameworks, and production-ready machine learning pipelines. The role will involve fine-tuning models, building RAG pipelines, improving prompt strategies, and collaborating with engineering teams to integrate AI capabilities into operational tools.

Key Responsibilities

• Fine-tune and optimize Large Language Models for domain-specific use cases.
• Build evaluation frameworks to measure model quality, accuracy, and performance.
• Develop and maintain RAG pipelines using operational data, documentation, and codebase references.
• Design prompt engineering strategies and improve multi-step AI workflows.
• Analyze model failure points, identify accuracy gaps, and recommend improvements.
• Collaborate with software engineering teams to deploy model enhancements into production.
• Create dashboards and reporting to track model performance and continuous improvement.

Requirements

• 3+ years of hands-on experience in machine learning, deep learning, NLP, or LLM development.
• Experience fine-tuning language models such as GPT, Claude, Llama, Mistral, or similar.
• Strong Python skills and experience with PyTorch, TensorFlow, or Hugging Face Transformers.
• Experience building end-to-end ML pipelines, including data preparation, training, evaluation, and deployment.
• Knowledge of model evaluation metrics such as accuracy, F1, BLEU, ROUGE, and human evaluation methods.
• Experience with RAG architectures, vector databases, and embedding models is preferred.
• Familiarity with AWS ML tools such as Amazon Bedrock or SageMaker is a plus.
• Ability to work independently and deliver high-quality results with minimal supervision.