Key Responsibilities

• Design and deploy deep learning models for NLP and computer vision applications.
• Customize and integrate LLMs like GPT or LLaMA into production systems.
• Create and manage Retrieval-Augmented Generation (RAG) pipelines with vector-based databases.
• Build scalable machine learning workflows in collaboration with data engineering teams.
• Conduct model testing, benchmarking, and continuous performance tuning.
• Utilize mathematical principles to enhance training efficiency and accuracy.
• Keep pace with current trends in machine learning and generative AI technologies.

Requirements

• 4–6 years of hands-on experience in machine learning or deep learning roles.
• Strong programming skills in Python, with experience in PyTorch or TensorFlow.
• Proficiency in using libraries such as Hugging Face Transformers.
• Practical experience with LLMs, vector databases like FAISS or Pinecone, and RAG models.
• Understanding of model deployment, containerization (e.g., Docker), and DevOps practices.
• Solid background in mathematics, particularly in statistics, linear algebra, and optimization.

Preferred Qualifications

• Knowledge of techniques such as quantization, model distillation, or distributed training.
• Familiarity with tools like MLflow, DVC, or Airflow for MLOps.
• Experience in OCR systems, multi-modal learning, or contributions to open-source projects.

Role Details

• Industry: Artificial Intelligence / Emerging Technologies
• Department: Data Science & Analytics
• Employment Type: Full Time, Permanent

Education

• UG: B.Tech/B.E. in Computer Science or a closely related discipline