| Senior ML Platform Engineer || 12+ || Remote at Remote, Remote, USA |
| Email: srivalli@fluxteksol.com |
|
From: Srivalli, Fluxteksolutions srivalli@fluxteksol.com Reply to: srivalli@fluxteksol.com Job Title: Senior ML Platform Engineer (Serving Infrastructure) Location: [San Jose/Remote Option] Department: ML Platform Engineering Exp: 12+years Visa: Any Visa is fine Role Overview: We're looking for an experienced engineer to build our ML serving infrastructure. You'll create the platforms and systems that enable reliable, scalable model deployment and inference. This role focuses on the runtime infrastructure that powers our production ML capabilities. Key Responsibilities: Design and implement scalable model serving platforms for both batch and real-time inference Build model deployment pipelines with automated testing and validation Develop monitoring, logging, and alerting systems for ML services Create infrastructure for A/B testing and model experimentation Implement model versioning and rollback capabilities Design efficient scaling and load balancing strategies for ML workloads Collaborate with data scientists to optimize model serving performance Technical Requirements: 12+ years of software engineering experience, with 4+ years in ML serving/infrastructure Strong expertise in container orchestration (Kubernetes) and cloud platforms Experience with model serving technologies (TensorFlow Serving, Triton, KServe) Deep knowledge of distributed systems and microservices architecture Proficiency in Python and experience with high-performance serving Strong background in monitoring and observability tools Experience with CI/CD pipelines and GitOps workflows Nice to Have: Experience with model serving frameworks: o TorchServe for PyTorch models o TensorFlow Serving for TF models o Triton Inference Server for multi-framework support o BentoML for unified model serving Expertise in model runtime optimizations: o Model quantization (INT8, FP16) o Model pruning and compression o Kernel optimizations o Batching strategies o Hardware-specific optimizations (CPU/GPU) Experience with model inference workflows: o Pre/post-processing pipeline optimization o Feature transformation at serving time o Caching strategies for inference o Multi-model inference orchestration o Dynamic batching and request routing Experience with GPU infrastructure management Knowledge of low-latency serving architectures Familiarity with ML-specific security requirements Background in performance profiling and optimization Experience with model serving metrics collection and analysis Keywords: continuous integration continuous deployment machine learning Senior ML Platform Engineer || 12+ || Remote srivalli@fluxteksol.com |
| srivalli@fluxteksol.com View All |
| 12:39 AM 08-Mar-25 |