Machine Learning Ops. (ML Ops) Engineer

Role: Machine Learning Ops. (ML Ops) Engineer

Location: Alpharetta, GA

Mode of Hire: Full Time

++Mandatory skills to have :++

· Knowledge in MLOps infrastructure with AWS.

· Well versed in AWS Sagemaker components that has some experience with mlops design.

· Strong Communication skills.

++Skills Required:++

• 4-8 years’ experience of applied machine learning 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.