Machine Learning Ops; MLOps Engineer

Assistant Manager - Talent Acquisition Team at Coforge

Role:
Machine Learning Ops Engineer

Mode of Hire:
Full Time

We need candidates who can come for In-Person interview at Alpharetta, GA

• 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 data 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., Sage Maker, Bedrock, Lambda, Lex, Cloud Watch, Cloud Trail, Redshift ML, Dynamo
  
  DB, Code Build, Code Deploy, S3, EC2, IAM, AMIs
• Proficient in API development using FastAPI, 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.

• Work closely with Onsite Lead, Data Scientists, Data Engineers, 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, pre‑trained 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 Sage Maker, MLFlow, and Cloud Watch 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 product ionize 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.

• Mid‑Senior level

• Full‑time

• Information Technology