About the role
Requirements:
- Bachelor's or Master's degree in Science or Engineering with strong programming, data science, critical thinking, and analytical skills.
- 5+ years of experience building in ML and Data science.
- Recent demonstrable hands-on experience with LLMs - integrating off-the-shelf LLMs, fine-tuning smaller models, building RAG pipelines, designing agentic flows, and other optimization techniques with LLMs.
- Strong conceptual understanding of foundational models, transformers, and related research.
- Strong conceptual understanding of the basics of machine learning and deep learning, with expertise in Computer Vision and Natural Language Processing.
- Recent demonstrable experience with managing large datasets for AI projects.
- Experience with implementing AI projects in Python and working knowledge of associated Python libraries - numpy, scipy, pandas, sklearn, matplotlib, nltk, etc.
- Experience with Hugging Face, Spacy, BERT, Tensorflow, Torch, OpenRouter, Modal, and similar services/frameworks.
- Ability to write clean, efficient, and bug-free code.
- Proven ability to lead initiatives from concept to operation while navigating challenges effectively.
- Strong analytical and problem-solving skills.
- Excellent communication and interpersonal skills.
- Recent experience with implementing state-of-the-art scalable AI pipelines for extracting data from unstructured / semi-structured sources and converting it into structured information, along with the necessary technical infrastructure to support deployment.
- Experience with cloud platforms (AWS, GCP, Azure), containerization (Kubernetes, ECS, etc. ), and managed services like Bedrock, SageMaker, etc.
- Experience with MLOps practices, e. g., model monitoring, feedback pipelines, CI/CD flows, and governance best practices.
- Experience working with applications hosted on AWS or Django web frameworks.
- Familiarity with databases and web application architecture.
- Experience working with OCR tools or PDF processing libraries.
- Completed academic or online specializations in Machine Learning or Deep Learning.
- Track record of publishing research in top-tier conferences and journals.
- Participation in competitive programming (e. g., Kaggle competitions) or contributions to open-source projects.
- Experience working with geographically distributed teams across multiple time zones.