Description :

Key Responsibilities :

- Design and build scalable LLM platforms with seamless integration capabilities.

- Develop, train, and deploy machine learning and generative AI models in production environments.

- Create and maintain connectors for various data sources to ensure efficient data flow and real-time processing.

- Deploy and manage business dashboards that provide real-time insights into LLM performance and usage.

- Conduct drift analysis to identify and mitigate deviations in model performance over time.

- Implement data integration strategies for managing large, complex datasets efficiently.

- Collaborate with cross-functional teams to integrate new use cases into existing LLM ecosystems.

- Establish monitoring frameworks and validation protocols to ensure model quality and reliability.

- Enhance user experience through intuitive UI tools and visualization components for model interaction.

Required Technical Skills :

- Proven experience building and scaling LLM or AI platforms (platform-level integration is a must)..

- Advanced proficiency in Python and popular machine learning/data science libraries (e.g., TensorFlow, PyTorch, Pandas, NumPy, Scikit-learn)..

- Strong experience with data integration, pipeline orchestration, and large dataset management.

- Knowledge of ML model validation techniques, drift detection, and performance optimization.

- Experience developing monitoring tools, analytics dashboards, and web-based UI interfaces.

- Familiarity with modern ML Ops practices, CI/CD pipelines, and cloud environments (AWS, GCP, or Azure)..

- Hands-on experience in LLM fine-tuning, API integration, and prompt engineering is highly desirable.

Preferred Qualifications :

- Bachelors or Masters degree in Computer Science, Artificial Intelligence, or Data Engineering.

- Experience with LangChain, Hugging Face Transformers, or OpenAI APIs.

- Prior exposure to Generative AI systems and enterprise-scale AI deployments.