Description

You will design and implement large language model architectures and multi-modal foundation model systems.

Responsibilities

• Develop model tuning pipelines and implement Reinforcement Learning from Human Feedback (RLHF).
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines and scalable systems.
• Integrate APIs from providers such as OpenAI and Cohere into production workflows.
• Manage MLOps for LLMs, including deployment, monitoring, and versioning.
• Apply fine-tuning techniques like LoRA and PEFT to foundational models.

Required Skills

• 5+ years of experience in machine learning or related fields.
• Expertise in Python and core libraries including NumPy, Pandas, and Scikit-learn.
• Hands-on experience with Transformers, Encoder/Decoder architectures, and HuggingFace.
• Proficiency in frameworks such as PyTorch, TensorFlow, LangChain, and LlamaIndex.
• Experience with data engineering workflows using Spark, Airflow, and SQL.
• Practical knowledge of prompt engineering and fine-tuning methodologies.
• Understanding of classical machine learning, including supervised and unsupervised learning.
• Familiarity with AI ethics, explainability, and governance.

Preferred Skills

• Experience with vector databases such as FAISS, Weaviate, or Pinecone.
• Knowledge of GPU compute infrastructure and distributed model training.

Key Skills

Education

Any Gradute