ML Ops Engineer

Role: ML Ops Engineer

Location: Alpharetta, GA



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

Financial Domain client



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.