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Apple LLM Ops Engineer India, Telangana, Hyderabad 180072061
Yesterday
KEY RESPONSIBILITIES:- Design and build scalable infrastructure for fine-tuning, and deploying large language models.- Develop and optimize inference pipelines using popular frameworks and engines (e.g. TensorRT, vLLM, Triton Inference Server).- Implement observability solutions for model performance, latency, throughput, GPU/TPU utilization, and memory efficiency.- Own the end-to-end lifecycle of LLMs in production—from experimentation to continuous integration and continuous deployment (CI/CD).- Automate and harden model deployment workflows using Python, Kubernetes, Containers and orchestration tools like Argo Workflows and GitOps.- Design reproducible model packaging, versioning, and rollback strategies for large-scale serving.- Stay current with advances in LLM inference acceleration, quantization, distillation, and model compilation techniques (e.g., GGUF, AWQ, FP8).
- 5+ years of experience in LLM/ML Ops, DevOps, or infrastructure engineering with a focus on machine learning systems.
- Advance level proficiency in Python/Go, with ability to write clean, performant, and maintainable production code.
- Deep understanding of transformer architectures, LLM tokenization, attention mechanisms, memory management, and batching strategies.
- Proven experience deploying and optimizing LLMs using multiple inference engines.
- Strong background in containerization and orchestration (Kubernetes, Helm).
- Familiarity with monitoring tools (e.g., Prometheus, Grafana), logging frameworks, and performance profiling.
- Experience integrating LLMs into micro-services or edge inference platforms.
- Experience with Ray distributed inference
- Hands-on with quantization libraries
- Contributions to open-source ML infrastructure or LLM optimization tools.
- Familiarity with cloud platforms (AWS, GCP) and infrastructure-as-code (Terraform).
- Exposure to secure and compliant model deployment workflows
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