ML Ops Engineer

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 CI/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