Principal Engineer, Machine Learning, SMAI
Tasks
- Architect and execute custom model training and fine tuning
- Build and maintain data and solution pipelines
- Create and maintain CI/CD pipelines for ML solutions
- Design and deploy autonomous AI agents
- Design and optimize data structures in data platforms
- Optimize training throughput and memory efficiency
- Orchestrate LLM interactions with internal tools and APIs
- Profile and debug GPU performance bottlenecks
Perks/Benefits
- N/A
Skills/Tech-stack
Agentic AI | Auto RL | Autogen | BF16 | Big Data | Big data processing | CI/CD | CUDA | Cloud platform | CrewAI | Data Pipelines | Data Processing | Data-parallel | DeepSpeed | Distributed Data Parallel | Distributed Training | Distributed data | Docker | ETL | FP16 | FSDP | Fine Tuning | GPU Architecture | GPU memory | GPU memory management | Generative AI | Git | Google Cloud | Google Cloud Platform | HPC job scheduling | Human Feedback | Java | Jenkins | Job Scheduling | Kubernetes | Langchain | Langgraph | Language Models | Large Language Models | Learning from Human Feedback | Llamaindex | LoRA | Machine Learning | Megatron-LM | Memory Management | Mixed Precision | Model Fine-tuning | Model Parallelism | Nsight Systems | PEFT | PEFT Techniques | Prompt engineering | PyTorch | PyTorch Profiler | Python | QLoRA | Ray | Reinforcement Learning | Reinforcement Learning from Human Feedback | Scikit-learn | Slurm | Snowflake | Supervised Fine Tuning | TensorFlow | TensorRT-LLM | Triton | VLLM
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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