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.

Created: 2026-03-04