Senior AI Infrastructure Engineer, LLM/AI Platforms
Tasks
- Analyze GPU utilization and memory bottlenecks
- Apply quantization batching caching
- Architect data platforms for LLMs RAG and agentic systems
- Build evaluation frameworks for model performance accuracy reliability
- Collaborate to transform research prototypes into production services
- Deploy and optimize inference frameworks
- Develop production ready code with testing and maintainability
- Document architecture designs and communicate technical decisions
- Implement MLOps DataOps monitoring observability recovery
- Manage LLM model lifecycle versioning checkpointing reproducibility
- Mentor engineers and strengthen technical capabilities
- Model and normalize AI ML data and semantic catalogs
- Optimize LLM inference serving infrastructure
- Provision GPU clusters for LLM training
Perks/Benefits
- Competitive vacation and holidays
- Comprehensive wellness programs
- Employee networks
- Paid adoption leave
- Paid parental leave
- Professional development opportunities
- Remote work
- Volunteer opportunities
- World-class office amenities
Skills/Tech-stack
AWS | Airflow | Amazon SageMaker | Ansible | Batching | CUDA | Caching | Checkpointing | Cloud platform | Data Modeling | Data Normalization | DataOps | Distributed Training | Docker | GPU | Google Cloud | Google Cloud Platform | Google Vertex | Google Vertex AI | Inference Server | Infrastructure as Code | JAX | Kubernetes | LLM Inference | LLM Training | MLOps | MLflow | Megatron | Model Evaluation | Model versioning | Monitoring | NVIDIA | OCI | Observability | PyTorch | Python | Quantization | RAG | Ray | Reproducibility | Retrieval-Augmented Generation | Semantic Cataloging | Slurm | TPU | Terraform | Triton Inference | Triton Inference Server | VLLM | Vertex AI | Zero Touch | Zero Touch Recovery | “as-code”
Education
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