We have an immediate opening for Senior AI Engineer | Remote. Please go through the requirement and reply with your updated profile, contact details, and your availability if you would be interested in it. A highly skilled Generative AI / Large Language Model Specialist with expertise in domain-specific dataset preparation, advanced fine-tuning, and multi-step agentic system design. The ideal candidate will have hands-on experience working with medical data, along with a strong background in reinforcement learning, reward modelling, and embedding optimization.
Key Responsibilities:
- Domain-Specific Dataset Preparation – Curate, preprocess, and structure medical chart datasets, including comprehensive patient records (medical history, diagnoses, treatments, test results) while adhering to compliance and privacy requirements.
- Tokenization for Specialized Data – Apply and optimize tokenization strategies tailored to domain-specific language, ensuring high model efficiency and accuracy.
- Advanced LLM Fine-Tuning – Perform parameter-efficient and full fine-tuning of LLMs, incorporating reinforcement learning with human feedback (RLHF) and reward modelling techniques.
- Model Evaluation & Benchmarking – Define and implement fine-tuned evaluation metrics, benchmark models, and analyse system performance to ensure reliability and accuracy.
- Agentic Architecture Design – Architect and implement multi-step, reasoning-capable AI agents, leveraging embedding tuning for optimal task execution.
Qualifications:
- Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related field
- Proven track record in medical NLP or domain-specific LLM projects.
- Strong understanding of transformer architectures, embedding models, and prompt engineering.
- Proficiency in Python and ML frameworks (e.g., PyTorch, Cuda).
- Familiarity with RLHF, reward modelling, and evaluation frameworks.
Nice to Have:
- Prior work in multi-agent AI systems.
- Experience with embedding Optimization vector databases and knowledge graphs.
- Contributions to open-source LLM projects.
Principal AI Engineer Expectations :
- Should be able to fine-tune models, especially in contexts where the architecture differs from standard setups. 1
- Must guide the team in reinforcement learning and resolve issues during fine-tuning, articulating solutions clearly. 2
- Needs experience in deep learning training; prior LLM fine-tuning is a plus but not mandatory, as deep learning experience is considered sufficient to understand LLM fine-tuning requirements. 3
- Should have expertise in advanced agentic AI, including multi-step RAG (Retrieval-Augmented Generation) solutions, architecting tools, MCP servers, writing advanced RAG code, and benchmarking both fine-tuned and RAG models. 4
