- ✔️ Build large-scale machine learning solutions for classification, regression, clustering, and recommendation tasks.
- ✔️ Develop automated training pipelines, feature stores, and data transformations.
- ✔️ Implement ML systems capable of handling high-volume real-time data.
- ✔️ Create reusable libraries and frameworks for ML model development.
- ✔️ Perform model training, validation, and performance tuning using cloud GPU/TPU resources.
- ✔️ Collaborate with Data Scientists to transform prototypes into production systems.
- ✔️ Deploy ML models through REST APIs, streaming services, or serverless functions.
- ✔️ Implement monitoring tools to track model health, latency, and production failures.
- ✔️ Build robust data validation, testing, and quality control checks.
- ✔️ Maintain and optimize ML infrastructure for scalability and cost efficiency.
- ✔️ Use version control and experiment tracking tools like MLflow or Weights & Biases.
- ✔️ Incorporate responsible AI frameworks for fairness, transparency, and explainability.
- ✔️ Collaborate with business teams to integrate ML predictions into workflows.
- ✔️ Optimize models for edge devices, mobile platforms, or low-latency environments.
- ✔️ Troubleshoot production ML issues and perform root-cause analysis.
- ✔️ Benchmark model performance using industry-standard datasets and metrics.
- ✔️ Explore and implement deep learning, transformers, and generative AI solutions.
- ✔️ Document ML architectures, workflows, and project implementation details.
- ✔️ Participate in code reviews and mentoring of junior engineers.
- ✔️ Drive innovation by proposing new ML methodologies and tools.
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