Requirements
- 3+ years of experience as an ML Engineer or similar role.
- Strong Python skills (scikit-learn, pandas, NumPy, matplotlib).
- Solid understanding of ML fundamentals (regression, classification, clustering, validation, metrics).
- Experience with TensorFlow, PyTorch, or Keras.
- Proven experience with AWS SageMaker.
- Familiarity with AWS Bedrock for foundational and generative models (LLM).
- Data preprocessing, feature engineering, and model evaluation experience.
- SQL knowledge and experience with structured/semi-structured data.
- ML deployment experience (FastAPI/Flask, Docker).
- Exposure to MLOps (pipelines, versioning, monitoring, reproducibility).
- Proficiency with Git.
- Strong analytical, communication, and problem-solving skills.
- Willingness to stay updated with ML and AI tools.
- English: Intermediate+.
- Desired: Experience with cloud platforms (AWS, GCP, Azure) and managed ML services (SageMaker, Vertex AI).
- Experience with MLFlow, DVC, Airflow, or similar tools.
- CI/CD for machine systems.
- Big data tools (Spark, Hadoop).
- Knowledge in data security and ethical AI.
- Experience with NLP/LLM, computer vision, or agent AI.