MLOps Engineer

Role:  ML Ops Engineer

Need candidates who can work onsite form Day 1 (5 days a week)

Skills Required:

•         4-8 years’ experience of applied machine learning/ML Ops in BFS / Investment Management industry

•         PhD or MS in Computer Science, Statistics or related field

•         Expertise in Machine Learning algorithms and frameworks:

·       Training and tuning pre-trained models

·       Working with structured and unstructured for Fraud models

•         Deep proficiency in Python with experience developing production-quality Python modules

•         Strong domain focus on fine-tuning and enhancing fraud detection models

•         Deploying models in AWS production environments

•         Strong command on AWS cloud stack with working knowledge of architecture components i.e., SageMaker, Bedrock, Lambda, Lex, CloudWatch, CloudTrail, Redshift ML, DynamoDB, CodeBuild, CodeDeploy, S3, EC2, IAM, AMIs

•         Proficient in API development using Fast API, Flask, etc. delivering asynchronous AI inference services and scalable API solutions for AI-powered applications.

•         Good command over statistical principles of data and model quality e.g., PSI, model performance metrics etc.

·       Roles and Responsibilities:

•         Work closely with Onsite Lead, Data scientists, Data Engineers, and QA and client stakeholders.

•         Evaluate input data for various statistical properties i.e., data drift using PSI and other metrics

•         Develop methods for monitoring data and models and efficient processes for updating or replacing old models with ones trained on new data or with the latest, state-of-the-art, pretrained models available

•         Skilled in evaluation metrics like precision, recall, F1-score, and AUC-ROC, ensuring high accuracy and precision in classification and regression models for Fraud.

•         Ensure right-fitting of architecture in AWS for the models at hand to optimize model inferencing

•         Strong working command of AWS SageMaker, MLFlow, and CloudWatch is a must

•         Should have hands on experience with deploying CI/CD Pipelines in AWS

•         Assist with documentation and governance of all ML and NLP pipeline artifacts

•         Find innovative solutions that increase automation and simplify work in AI workflows

•         Refactor and productionize research code, models and data while maintaining the highest levels of deployment practices including technical design, solution development, systems configuration, test documentation/execution, issue identification and resolution.