The Senior MLOps Engineer designs, develops, and implements machine learning models and statistical algorithms to address business problems. Responsibilities include the complete model development lifecycle, conducting exploratory data analysis, collaborating with cross-functional teams, mentoring junior data scientists, and ensuring model deployment and scalability on cloud platforms.
Senior MLOps Engineer
Primary Skills
- Skills – ML, MLOps, statistical machine learning, traditional ML , predictive modelling, classical ML models (such as bagging, boosting, regression, classification, etc.), data visualization, and Python (with pandas and PySpark SQL expertise).
Job requirements
- Years of experience- 6+ Years
- Role- Senior MLOps Data Engineer
- Primary Skills Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(Kubeflow, Bantom), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio.
- Key Responsibilities: Model Development & Implementation: Design, develop, and implement machine learning models and statistical algorithms to solve business problems (e.g., predictive modeling, classification, recommendation systems, NLP).
- Own the end-to-end lifecycle of model development: from data exploration and feature engineering to model training, deployment, and monitoring.
- Data Analysis & Insights Generation: Perform in-depth exploratory data analysis (EDA) to identify trends, patterns, and opportunities. Translate complex data into clear and actionable business insights.
- Business Collaboration: Partner with stakeholders (Product, Engineering, and Business teams) to understand requirements and deliver impactful data science solutions.
- Communicate findings and model outcomes effectively to technical and non-technical audiences.
- Technical Leadership: Guide and mentor junior data scientists in best practices, model optimization, and advanced techniques.
- Act as a thought leader, contributing to the strategic roadmap of data science initiatives.
- Scalability and Deployment: Collaborate with Data Engineers and ML Engineers to productionize models using cloud platforms (AWS, GCP, Azure) and MLOps frameworks. Ensure models are robust, scalable, and integrated with business workflows.
- Continuous Improvement: Monitor model performance post-deployment and iterate on models to improve accuracy and business value. Stay up-to-date with the latest tools, techniques, and trends in machine learning and data science.
- Key Skills Machine Learning (Supervised/Unsupervised) Python/R, TensorFlow, PyTorch, Scikit-learn Data Analysis and Feature Engineering SQL, Spark, Big Data Technologies Cloud Platforms (AWS, Azure, GCP) A/B Testing and Statistical Analysis Excellent Communication and Collaboration Skills
Top Skills
AWS
Azure
Bantom
GCP
Kubeflow
Pyspark
Python
PyTorch
R
SAS
Scikit-Learn
Spss
SQL
TensorFlow
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