Job Description
85% accuracy ● Fine-tuned embedding model powering memory search
● ML pipeline processing 10 K+ conversations daily with 6 Months :
● Edge-optimized models reducing cloud inference costs by 60%
● Proactive insight detection using unsupervised learning
● Multi-agent workflows with trained routing and arbitration
● A/B testing infrastructure validating model improvements
12 Months :
● Automated retraining pipelines maintaining model quality
● You've built an ML engineering team
● Core AI systems are defensible competitive moats
● Models outperform generic foundation models on domain tasks
Who You Are
Must-Have:
● 2-5 years building and deploying ML/DL models in production serving real users at scale
● Strong Py Torch or Tensor Flow expertise : training, optimization, debugging, deployment
● End-to-end ML ownership : data pipeline → model training → production → monitoring → iteration
● Deep learning fundamentals : architectures (CNNs, RNNs,...
● ML pipeline processing 10 K+ conversations daily with 6 Months :
● Edge-optimized models reducing cloud inference costs by 60%
● Proactive insight detection using unsupervised learning
● Multi-agent workflows with trained routing and arbitration
● A/B testing infrastructure validating model improvements
12 Months :
● Automated retraining pipelines maintaining model quality
● You've built an ML engineering team
● Core AI systems are defensible competitive moats
● Models outperform generic foundation models on domain tasks
Who You Are
Must-Have:
● 2-5 years building and deploying ML/DL models in production serving real users at scale
● Strong Py Torch or Tensor Flow expertise : training, optimization, debugging, deployment
● End-to-end ML ownership : data pipeline → model training → production → monitoring → iteration
● Deep learning fundamentals : architectures (CNNs, RNNs,...
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