Simarn Solutions
Job Role: Machine Learning Engineer (MLOps)
Location: Austin, Texas (Onsite)
Type: 1099 Contract | C2C
Job Description
Experienced Machine Learning Engineer with 8+ years of hands‑on expertise deploying and scaling machine learning models in production environments.
Skilled in operationalizing complex models and integrating them into enterprise systems with a focus on performance, scalability, and governance.
Partner with data science and engineering teams to deliver, optimize, and maintain production‑grade ML models and pipelines.
Deploy and manage end‑to‑end machine learning workflows, from model development to operational monitoring.
Proficient in core ML algorithms such as Regression, Classification, and Natural Language Processing (sentiment analysis, topic modeling, TF‑IDF).
Experienced with tools and frameworks including Scikit‑learn, VADER Sentiment, Pandas, and PySpark.
Design and maintain dynamic data pipelines tailored to specific use cases.
Integrate machine learning solutions within business workflows, ensuring seamless coordination across upstream and downstream systems.
Develop and automate reporting pipelines for model performance metrics to support Model Risk Oversight and governance reviews.
Create and maintain runbooks for ongoing model support, versioning, and operational maintenance.
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Location: Austin, Texas (Onsite)
Type: 1099 Contract | C2C
Job Description
Experienced Machine Learning Engineer with 8+ years of hands‑on expertise deploying and scaling machine learning models in production environments.
Skilled in operationalizing complex models and integrating them into enterprise systems with a focus on performance, scalability, and governance.
Partner with data science and engineering teams to deliver, optimize, and maintain production‑grade ML models and pipelines.
Deploy and manage end‑to‑end machine learning workflows, from model development to operational monitoring.
Proficient in core ML algorithms such as Regression, Classification, and Natural Language Processing (sentiment analysis, topic modeling, TF‑IDF).
Experienced with tools and frameworks including Scikit‑learn, VADER Sentiment, Pandas, and PySpark.
Design and maintain dynamic data pipelines tailored to specific use cases.
Integrate machine learning solutions within business workflows, ensuring seamless coordination across upstream and downstream systems.
Develop and automate reporting pipelines for model performance metrics to support Model Risk Oversight and governance reviews.
Create and maintain runbooks for ongoing model support, versioning, and operational maintenance.
#J-18808-Ljbffr