You will be part of a team focused on re-training, model hosting, cost optimization, and managing production workflows at scale.

• Build and maintain pipelines for model fine-tuning and retraining, including LoRA-based workflows

• Integrate and maintain vector search services and semantic similarity infrastructure

• Design scalable model serving solutions for open-source and foundation models

• Develop systems for experiment tracking, model versioning, and evaluation

• Monitor production models for drift and performance degradation

• Manage compute cost and resource optimization across distributed training jobs

• Integrate Human-in-the-Loop (HITL) workflows and offline labeling into training pipelines

• Support model deployment for varied model architectures, including Vision-Language Models, Convolutional Neural Nets, and Embedding Generation models

• Stand up and maintain Feature Store and data versioning infrastructure

• Architect and implement RAG pipelines for video metadata, summarization, and Q&A

• Build evaluation frameworks to assess LLM performance, hallucination frequency, and structured response accuracy

What to Bring:

• 9+ years of experience in machine learning engineering, with end-to-end ML workflow expertise

• Strong background in model retraining, fine-tuning, and evaluation techniques

• Experience deploying and managing open-source model servers (e.g., Triton, TorchServe, Ray Serve)

• Proficient in managing cost-effective distributed computing environments (e.g., Kubernetes, Ray, SageMaker)

• Familiar with experiment tracking tools (e.g., MLflow, Weights & Biases) and model versioning strategies

• Deep understanding of ML domains including NLP, RecSys, and reinforcement learning

• Experience with real-time inference systems and streaming data pipelines is a plus

• Familiarity with labeling tools, HITL workflows, and offline data curation strategies

  • Comfort working in Agile development environments and collaborating across global teams