Engineer - Machine Learning

Collaboration

Work with product managers and business owners to understand requirements.

Data to Model

1. Create data pipelines and preprocess data from diverse sources (text, imaging, genomics, HER, etc.) to make it ready for model training.
2. Monitor data quality through the process.
3. Build scalable and efficient ML pipelines powered by GPUs.
4. Implement a comprehensive model evaluation mechanism to measure and enhance model performance for superior metrics.

Model Enhancement and System Augmentation

1. Augment capabilities of the foundation model using agent-based tooling such as LangChain and LlamaIndex.
2. Implement high-performant vector databases, knowledge graphs, reasoning engines, and other similar components to augment the capabilities of a foundation model.

Deployment, Testing, and Prototyping

1. Deploy the AI models in a highly available and scalable manner.
2. Create model APIs for the software developers to consume in their applications.
3. Implement A/B testing mechanisms.
4. Implement guards against unintended inputs or unsupported usage of the system.
5. Prototype and present demo-ready AI solutions to various stakeholders.

Qualifications Experience, Education, Skills

1. 3+ years of ML Engineering experience.
2. Experience of building models using structured and unstructured data.
3. Bachelor's/Master's degree in Computer Science, Machine Learning, AI, or related fields.
4. Experience with building products powered by NLP models and hands-on experience with MLOps tools, e.g., MLFlow.