JD:
- Education: Bachelors/Masters from a top-tier institute (IIT/Tier-1 etc.) in Computer Science AI or related field.
- 1 5 years in applied ML/AI roles
- Expert in Python strong in at least one deep-learning framework (PyTorch/TensorFlow).
- Experience with end-to-end ML pipelines (data prep training evaluation deployment monitoring).
- Proven success shipping ML-powered products not just models to real users.
- Hands-on with MLOps tooling (MLflow Weights & Biases DVC Airflow Prefect etc.).
- Knowledge of containerized deployments (Docker ECS K8s) and CI/CD for ML.
- Strong fundamentals in statistics experimentation and interpreting real-world feedback.
- Experience optimizing/operating GPU inference (ONNX TensorRT mixed precision batching).
- Familiar with vector databases RAG pipelines and LLM fine-tuning/adapters (LoRA/QLoRA).
- Exposure to observability stacks (Prometheus/Grafana/OpenTelemetry) and production logging/metrics.
JD: Education: Bachelors/Masters from a top-tier institute (IIT/Tier-1 etc.) in Computer Science AI or related field. 1 5 years in applied ML/AI roles Expert in Python strong in at least one deep-learning framework (PyTorch/TensorFlow). Experience with end-to-end ML pipelines (data prep traini...
JD:
- Education: Bachelors/Masters from a top-tier institute (IIT/Tier-1 etc.) in Computer Science AI or related field.
- 1 5 years in applied ML/AI roles
- Expert in Python strong in at least one deep-learning framework (PyTorch/TensorFlow).
- Experience with end-to-end ML pipelines (data prep training evaluation deployment monitoring).
- Proven success shipping ML-powered products not just models to real users.
- Hands-on with MLOps tooling (MLflow Weights & Biases DVC Airflow Prefect etc.).
- Knowledge of containerized deployments (Docker ECS K8s) and CI/CD for ML.
- Strong fundamentals in statistics experimentation and interpreting real-world feedback.
- Experience optimizing/operating GPU inference (ONNX TensorRT mixed precision batching).
- Familiar with vector databases RAG pipelines and LLM fine-tuning/adapters (LoRA/QLoRA).
- Exposure to observability stacks (Prometheus/Grafana/OpenTelemetry) and production logging/metrics.
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