Applied ML - Engineer

Key Responsibilities: Development and training of foundational models across modalities End to end lifecycle management of foundational model development from data curation to model deployment by collaborating with the core team members Conduct research to advance model accuracy and efficiency. Implement state-of-the-art AI techniques in Text/Speech and language processing. Collaborate with cross-functional teams to build robust AI stacks and integrate them seamlessly into production pipelines. Develop pipelines for debugging, CI/CD and observability of the development process. Demonstrated ability to lead projects and provide innovative solutions. Should document technical processes, model architectures, and experimental results., maintain clear and organized code repositories. Education: Bachelor’s or Master’s in any related field with 2 to 5 years of experience in industry in applied AI/ML. Minimum Requirements: Proficiency in Python programming and familiarity with 3-4 from the list of tools specified below: Foundational model libraries and frameworks (TensorFlow, PyTorch, HF Transformers, NeMo, etc) Experience with distributed training (SLURM, Ray, Pytorch DDP, Deepspeed, NCCL, etc) Inference servers (vLLM) Version control systems and observability (Git, DVC, MLFlow, W&B, KubeFlow) Data analysis and curation tools (Dask, Milvus, Apache Spark, Numpy) Text-to-Speech tools (Whisper, Voicebox, VALL-E (X), HuBERT/Unitspeech) LLMOPs Tools, Dockers etc Hands on experience with AI application libraries and frameworks (DSPy, Langgraph, langchain, llamaindex etc.) Show more Show less