Sr. Machine Learning Engineer IV

Employment Type:
Full-time

Experience:
6+ Years

Required Skills
:

Experience with

• Tensor Flow and PySpark.
• Convolutional Neural Networks (CNN) and Natural Language Processing (NLP).
• Logistic and Linear Regression Models.
• K-Means clustering.
• Spark, Hadoop, Hive, and Oozie.
• Jenkins and Selenium.
• Python, Java, and R.
• AWS Cloud.
• DB2, Oracle, Tableau, Qliksense, and Qlikview.

Master’s degree with 6 years of experience or Bachelor’s degree with 8 years of experience;
Majors:
Computer Science, Data Science, or equivalent.

Responsibilities
:

• Responsible for end-to-end software and data architecture design for large-scale enterprise solutions, serving as a Subject Matter Expert (SME) for high-performance technology stacks including Python, PySpark, Hive, AWS, and GCP.
• Act as the primary technical expert for evaluating emerging data technologies and architecting complex network health forecast models, telemetry options, and performance monitoring systems.
• Provide technical leadership in feasibility studies for Network Management Software, ensuring infrastructure and data pipelines can scale to handle massive, multi-terabyte datasets across hybrid-cloud environments.
• Supervise the team’s development methodology, perform final code reviews, and manage commit workflows for high-volume repositories to maintain rigorous standards.
• Architect and govern end-to-end AI/ML pipelines, ranging from automated data integrity checks and Exploratory Data Analysis (EDA) to advanced feature engineering and scalable model deployment.
• Design predictive models utilizing Logistic and Linear Regression, K-means clustering, CNNs, and other leading algorithms to forecast customer satisfaction, network equipment faults, and financial claim damage.
• Lead the migration and optimization of ML models from on-premise clusters to hyperscaler environments, including Google Cloud, ensuring performance benchmarks and model accuracy are maintained.
• Implement Natural Language Processing (NLP) classifiers using Tensor Flow and Jupyter Notebooks to synthesize unstructured data into actionable business intelligence.
• Define enterprise data warehousing needs and design sophisticated data models and repository structures utilizing Oracle, DB2, and Hive to support the full system development life cycle.
• Direct the design of complex data transformations and preprocessing frameworks that enable the automation of repeatable actions through models predicting system behavior in altered conditions.
• Synthesize functional requirements into high-level technical specifications for large-scale data mining and predictive propensity modeling.
• Guide teams on industry best practices for data lifecycle management, including high-fidelity test data acquisition and maintaining data integrity across integrated systems.
• Design and develop specialized, enterprise-level scripts (Python, Unix Shell, SQL) for the administration of complex communication networks and event-based automated response systems.
• Implement rapid-prototyping platforms using custom Python utilities for high-speed text classification, document comparison, and automated financial report validation.
• Enable continuous integration and deployment (CI/CD) for data-driven applications by monitoring cloud-based deployments and optimizing application performance benchmarks.
• Work on applications using:
  Tensor Flow and PySpark;
  Convolutional Neural Network (CNN) and Natural Language Processing (NLP);
  Logistic and Linear Regression Models; K-Means clustering;
  Spark, Hadoop, Hive, and Oozie;
  Jenkins and Selenium;
  Python, Java, and R; AWS Cloud; DB2, Oracle, Tableau, Qliksense, and Qlikview.
• Other similar duties as assigned.