Role Purpose
Design and deliver scalable, reliable, and production-ready AI/ML solutions, with end-to-end ownership from problem definition to deployment
and monitoring. Ensure seamless integration with product systems and data platforms while continuously improving model performance and
engineering standards.
Job Description
- Design and deliver end-to-end AI/ML solutions, ensuring models are scalable, reliable, and ready for production use.
- Translate business and technical problems into structured AI/ML problem statements.
- Collaborate closely with cross-functional teams to ensure alignment and successful implementation.
- Work closely with the AI/ML Engineer Lead to align on technical direction and solution design.
- Develop and optimize models across various use cases (CV, time-series forecasting, optimization, and LLM/RAG-based systems).
- Build and maintain end-to-end data pipelines, including data ingestion, preprocessing, and feature engineering.
- Optimize models for production, focusing on performance, latency, and resource efficiency.
- Integrate AI/ML models into backend systems or platforms (APIs, batch processing, streaming pipelines).
- Evaluate model performance using real-world data and manage trade-offs between accuracy, efficiency, and reliability.
- Monitor, retrain, and maintain models to ensure long-term performance and stability.
- Document experiments, pipelines, and models to ensure reproducibility and maintainability.
- Contribute to improving AI/ML standards, including modeling, experimentation, deployment, and monitoring practices.
Job Requirements
- Bachelor's or Diploma degree in Computer Science, Data Science, AI, or a related field.
- Minimum 5 years of experience in AI/ML development, particularly in building production-ready systems.
- Strong ownership of the full AI/ML lifecycle, from experimentation to production.
- Ability to adapt across multiple use cases and domains (CV, forecasting, LLM) based on business needs.
- Proficiency in Python and experience with ML frameworks (PyTorch, TensorFlow, or similar).
- Strong understanding of multiple AI/ML domains, such as computer vision (object detection, OCR), time-series forecasting (LSTM, TCN, PatchTST), machine learning (tree-based models LightGBM), optimization/decision models (RL or heuristics), and LLM/RAG systems.
- Strong ability to select appropriate models, methods, and approaches based on problem context and data characteristics.
- Experience building and managing end-to-end data pipelines (ETL, preprocessing, feature engineering).
- Experience deploying models into production environments (APIs, batch processing, streaming systems).
- Strong understanding of model evaluation, performance optimization, and trade-offs in real-world scenarios.
- Familiarity with data infrastructure and enterprise data systems is a plus.
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