Sr. Machine Learning Engineer IV

Softility, Inc.; Herndon (VA); On-site; USD 151,000 - 205,000; Full time

Job summary

Softility, Inc. is seeking a Sr. Machine Learning Engineer IV to design and oversee end-to-end ML and data architectures for large-scale enterprise solutions in a hybrid-cloud environment.

You will act as the SME for Python, PySpark, Hive, AWS, and GCP, guiding feasibility studies and scalable network health forecast models. You will lead architecture and data-modeling efforts, oversee code reviews, and ensure data pipelines scale to massive datasets.

Qualifications

• End-to-end ML/data architecture for enterprise-scale systems.
• Experience leading teams and performing final code reviews for high-volume repositories.
• Migration of ML models to cloud hyperscalers (AWS/GCP) with performance benchmarks.

Responsibilities

• Lead end-to-end ML pipelines from data prep to deployment.
• Architect enterprise data models and warehouses (Oracle/DB2/Hive).
• Evaluate new data technologies and drive scalable solutions.
• Oversee CI/CD and production deployment processes.

Skills

End-to-end architecture

Technical leadership

Feasibility studies

Data pipeline design

Model deployment

Code reviews

Tools

Python

PySpark

Hive

AWS

GCP

TensorFlow

Jupyter Notebooks

Oracle

DB2

Spark

Hadoop

Oozie

Jenkins

Selenium

SQL

Job description

[Sr. Machine Learning Engineer IV - Herndon, VA. 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 teams 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 TensorFlow 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: TensorFlow 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. M-F; 40 hrs/week. $178,214/year.
{#docs-internal-guid-f79d51ec-7fff-c6fc-ac84-a74d1ea0d203}