Skills/ Qualifications Required:
Relevant experience in ML Engineering/ ML Ops role with an end-to-end understanding of ML based project’s solution design & architecture, development, implementation & deployment
Should fulfill all the standard MLOps level 2 requirements for CI/CD + CT pipeline automation
Strong grasp & hands on experience with production ready scalable code using SQL (advance) and Python
Hands-on experience in working on any of the of cloud stacks: AWS/ Azure/ GCP
Good communication skills
- Can work hands-on independently.
Bachelor’s degree from Tier I/II colleges preferred
Job Responsibilities
- Actively own & manage client deliverables.
- Design solution architectures and pipelines for ML applications.
- Create ML prototypes, design ML systems, develop automated ML application pipelines (across data collection, processing, cleaning, transformation etc. aspects) under the constraints of scalability, correctness, and maintainability.
- Implement model evaluation and model + data validation tools/ techniques such as schema validation, evaluation metrics etc.
- Develop and deploy fCI/CD based automated ML application pipelines (collection, processing, cleaning, transformation etc.) along with the CT component for continuous feedback loop for re-training.
- Strong skills in Feature store setup, Pipeline Integration, Automated triggering, Model Continuous Delivery, Model Serving (via APIs) & Model Monitoring
Responsible for productionizing and making the models available as APIs / micro services.
Promote a practice of unifying system development (Dev) and system operations (Ops)
- Ensure output’s thorough quality check & provide analytics driven insights and next steps.
- To perform statistical analysis and fine-tune models using test results.
- Understand data and different platforms used by the client.
- Actively contribute towards problem solving & mentor juniors in the team
