Ai Ml Engineer

Role & responsibilities

• Model Development: Design, build, and deploy end-to-end machine learning pipelines using Python and deep learning frameworks like Tensor

Flow or Py

Torch.

• Time-Series Excellence: Develop advanced forecasting models (e.g., LSTMs, Transformers, Prophet, or Arima-based hybrids) to solve complex temporal data challenges.

• Productionalization: Optimize ML models for performance and scalability in production environments, including CI/CD integration and monitoring.

• Leadership & Collaboration: Mentor junior engineers and collaborate with stakeholders to translate business requirements into technical AI solutions.
  Technical Requirements

• Experience: 5 - 7 years of professional experience in Machine Learning or Data Science, with a proven track record of deploying models to production.

• Programming: Mastery of Python and its data stack (Pandas, Num

Py, Scikit-learn).

• Deep Learning: Strong expertise in Tensor

Flow or Py

Torch, specifically for sequential data architectures.

• Domain Knowledge: Advanced understanding of time-series analysis, including stationarity, seasonality, and multivariate forecasting.

• Engineering: Experience with SQL/NoSQL databases, cloud platforms (AWS/GCP/Azure), and containerization (Docker/Kubernetes).
  Preferred Qualifications

• Experience with MLOps tools (MLflow, Kubeflow, or DVC).

• Background in signal processing or financial modeling.

• Contributions to open-source ML projects or published research in relevant fields.