Back
Tier-1 brand, mid-level ML title, metro location, and broad LLM/MLOps skillset drive high competition.
ML, MLOps, and LLM expertise are transferable across industries, though regulated-domain experience moderately favors candidates.
Explicit 5–8 years plus mandatory ML, MLOps, AWS, and production experience makes screening highly strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, deploy, and support scalable machine learning models and pipelines including forecasting, classification, anomaly detection, and recommendations.
Implement MLOps practices such as experiment tracking, model versioning, CI/CD, automated testing, monitoring, and automated retraining primarily on AWS.
Develop and operate Generative AI/LLM applications along with monitoring, model evaluation, and collaboration across cross-functional technical teams.
5–8 years of experience in Software Engineering, Data Science, Machine Learning Engineering, Computer Science, IT, or related field.
Strong hands-on experience with Python, SQL, machine learning libraries (e.g., Scikit-learn, PyTorch, TensorFlow, XGBoost) and AWS cloud services (e.g., SageMaker, S3, Lambda).
Experience with predictive modeling, time-series forecasting, feature engineering, model training, evaluation, and deploying production ML models.
Work Experience Required: 5–8 years
Experienced in end-to-end ML model lifecycle management including MLOps and cloud-based deployments primarily on AWS.
Capable of building and operationalizing advanced forecasting and Generative AI/LLM applications with knowledge of LLM frameworks and retrieval-augmented generation.
Ability to work in agile, cross-functional teams collaborating closely with data engineers, scientists, software engineers, and business stakeholders.