Roles and responsibilities :
- Part of Data Science team to develop high impact, scalable and sustainable ML solutions
- Collaborate with Senior Data Scientists and product managers to understand customer needs and co-own the deployment and maintenance of key ML models
- Work closely with Data Scientists on text and image data using the leading ML techniques in GenAI/OCR/image processing
- Develop end to end pipeline and further enhancement of solutions for scale and stability
- Build quality checks, unit tests and diagnostic reports for code and output quality monitoring using python, SQL and yaml
- Conduct modeling experiments and report automation
- Scheduling, automating and stabilizing the current AWS pipeline using jenkins, Step Function, Lambda, Runtime, Sagemaker
- Develop in-house capability to deploy custom models on Sagemaker endpoints, taking care of platform and library compatibility Explore GCP capabilities to deploy projects - Code Versioning, end points, GCS, Bigquery and automation/scheduling
Preferred Qualifications :
- Curiosity to learn Advanced ML, Data Science + ML Ops
- 2-4 years working experience as a ML Engineer
- Hands-on experience with Python and SQL is must
- Understanding of data modeling, data access, data storage, and optimization techniques
- Experience working with cloud-based technologies and development processes
- An ideal candidate would be expected to have basic familiarity of GCP and AWS infrastructure. Advanced knowledge of MLOps-related tools like Sagemaker, ECR, Step Functions, Vertex AI is preferred
- Ability to quickly understand the tech pipeline/project infrastructure and engage deeply with Team members. Delivery quickly on new development or optimization related action items
- Quick action oriented approach preferred over brainstorming on very long term ideas
