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Translate data science prototypes into production-grade ML services and pipelines.-
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Build training and inference code with reproducibility, versioning, and automated testing.-
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Implement scalable model serving (online/offline), batching, and latency/throughput optimization.-
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Integrate model lifecycle tooling (tracking, registry, deployment automation, monitoring).-
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Collaborate with Data Engineering on feature pipelines and data contracts.-
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Own production health drift detection, performance regression, rollback strategies, and incident response.
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5+ years software engineering with 2+ years shipping ML models to production.-
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Strong Python skills and experience with ML frameworks (TensorFlow/PyTorch).-
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Experience with containers and orchestration (Docker/Kubernetes) and API development.
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Understanding of ML system design (data leakage, training-serving skew, drift).
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CI/CD and DevOps practices applied to ML workloads (MLOps).
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Experience with feature stores, model registries, and model monitoring stacks.
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GPU optimization and distributed training experience.
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Experience with responsible AI toolkits and compliance requirements.
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Python, TensorFlow, PyTorch, Docker, REST APIs