ML Operations Engineer

Skills/ Qualifications Required:

• Relevant experience in ML Engineering/ ML Ops role with an end-to-end understanding of ML based projects 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