ML Engineer | LLM & NLP Enthusiast | AI Researcher
πΉ Role: Machine Learning Engineer @ Chubb Business Services India
πΉ Education: B.E. in Electronics & Electrical Engineering, BITS Pilani
πΉ Research: Deep Learning, NLP, Code-Switching NLP (ACL, AACL-IJCNLP, IALP, AISC, ACIIDS)
πΉ Interests: LLM reasoning, Retrieval-Augmented Generation (RAG), Efficient Inference
- π Speculative Decoding for ultra-low-latency LLM outputs
- βοΈ Hierarchical Loss Functions to speed up convergence
- π Distributed inference and system-level optimizations
Project | Description | Status |
---|---|---|
π΅οΈ WhoDunIt | Benchmark for deduction & reasoning in LLMs. | β Live |
βοΈ EfficientLLM | Speculative decoding & hierarchical loss modules. | π§ WIP |
Languages & Frameworks
Python | Java | C++ | JavaScript PyTorch | TensorFlow | Keras | Transformers DeepSpeed | vLLM | OpenAI API | LangChain
Infrastructure & MLOps
Azure ML Studio | Databricks | GCP Vertex AI | AWS SageMaker MLflow | Kubeflow | Docker | Kubernetes | AKS
Data & DevOps
PostgreSQL | MongoDB | Apache Spark | Kafka | Redis Git, GitHub Actions, Jenkins | Linux | Bash | Jupyter
- WhoDunIt: Culprit detection benchmark (ACL ARR, 2024)
- Code-Switching NLP (IALP, 2023)
- Statistical Text Augmentation (ACIIDS, 2023)
- MALM: Zero-Shot MT (AACL-IJCNLP, 2022)
- Essay Scoring with Transformers (AISC, 2023)
βAI is our most powerful toolβletβs use it wisely!β π