LLM Training & Model Development Engineer

Skills

• Strong Python expertise with hands-on experience in PyTorch, Hugging Face Transformers, and LangChain.
• Deep understanding of LLM architectures, tokenizer mechanics, and parameter-efficient fine-tuning.
• Proficiency in data processing frameworks (Spark, Airflow, Pandas, Arrow, Dask).
• Experience with distributed training and GPU/TPU optimization (CUDA, NCCL).
• Knowledge of evaluation metrics and human-aligned reward modeling.
• Experience with Vector Databases (FAISS, Milvus, Pinecone) for context retrieval.
• Familiarity with cloud platforms (AWS, GCP, Azure) and container orchestration (Docker, Kubernetes).
• Exposure to agentic AI frameworks and feedback-based continuous improvement systems is a plus.
• Prior experience contributing to open-source LLM projects.
• Knowledge of data privacy, ethical AI, and prompt alignment techniques.

Qualifications

• Master’s or Ph.D. in Computer Science, AI, or related field preferred.
• A home-grown, domain-specialized LLM trained on proprietary and public datasets.
• A scalable fine-tuning pipeline that powers multiple downstream agents and AI applications.
• An autonomous model training framework capable of learning from feedback in real time.