About the Role
We're seeking a motivated and talented professional to develop and enhance our data infrastructure.
This is an opportunity to collaborate with a dynamic team of data scientists in big data analytics and applied AI. If you have a passion for designing and implementing advanced machine learning models, particularly in Generative AI, this role is a good fit.
We're looking for a versatile Machine Learning Engineer/Data Scientist to join our big-data analytics team. In this hybrid role, you'll design and prototype novel ML/DL models and productionize them end-to-end, integrating your solutions into our data pipelines and services.
Key Responsibilities
* Design, train, and validate supervised and unsupervised models (e.g., anomaly detection, classification, forecasting)
* Architect and implement deep learning solutions (CNNs, Transformers) with PyTorch
* Develop and fine-tune Large Language Models (LLMs) and build LLM-driven applications
* Implement Retrieval-Augmented Generation (RAG) pipelines and integrate with vector databases
* Build robust pipelines to deploy models at scale (Docker, Kubernetes, CI/CD)
Data Engineering & MLOps
* Ingest, clean, and transform large datasets using libraries like pandas, NumPy, and Spark
* Automate training and serving workflows with Airflow or similar orchestration tools
* Monitor model performance in production; iterate on drift detection and retraining strategies
* Implement LLMOps practices for automated testing, evaluation, and monitoring of LLMs
Software Development Best Practices
* Write production-grade Python code following SOLID principles, unit tests, and code reviews
* Collaborate in Agile ceremonies; track work in JIRA
* Document architecture and workflows using PlantUML or comparable tools
Cross-Functional Collaboration
* Communicate analysis, design, and results clearly
* Partner with DevOps, data engineering, and product teams to align on requirements and SLAs