Sr. Machine Learning Engineer

K-Tek Resourcing LLC
Austin, United States of America
20 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Austin, United States of America

Tech stack

API
Artificial Intelligence
Amazon Web Services (AWS)
Data analysis
Computer Vision
Azure
Big Data
Cloud Computing
Data Cleansing
Information Engineering
Distributed Systems
Hadoop
Python
Machine Learning
Natural Language Processing
NumPy
TensorFlow
Standard Sql
SQL Databases
Jupyter Notebook
Google Cloud Platform
Feature Engineering
PyTorch
Spark
Deep Learning
Model Validation
GIT
Pandas
Scikit Learn
Kubernetes
Machine Learning Operations
Data Pipelines
Docker
Databricks

Job description

  • Design, develop, train, and deploy machine learning models to solve business problems.
  • Build and maintain scalable data pipelines for collecting, processing, and transforming large datasets.
  • Perform feature engineering, data preprocessing, and exploratory data analysis.
  • Evaluate and optimize model performance using appropriate metrics and validation techniques.
  • Implement and fine-tune algorithms using frameworks such as PyTorch, TensorFlow, and Scikit-learn.
  • Develop and maintain production-ready ML solutions and APIs.
  • Collaborate with data scientists, data engineers, software developers, and business stakeholders to translate requirements into AI-driven solutions.
  • Monitor deployed models, identify performance degradation, and retrain models as needed.
  • Apply statistical analysis and mathematical modeling techniques to derive insights and improve predictions.
  • Automate model training, testing, and deployment processes using MLOps practices and CI/CD pipelines.
  • Work with cloud platforms such as AWS, Azure, or Google Cloud Platform to build and manage ML infrastructure.
  • Utilize distributed computing and big data technologies such as Spark, Databricks, and Hadoop for large-scale model training.
  • Develop and optimize SQL queries for extracting and analyzing structured data.
  • Create clear technical documentation, model reports, and presentations for stakeholders.
  • Ensure model governance, data quality, security, and compliance with organizational standards.
  • Research and evaluate new machine learning techniques, tools, and emerging AI technologies.

Requirements

  • Python, SQL
  • PyTorch, TensorFlow, Scikit-learn
  • Pandas, NumPy, Jupyter Notebook
  • Statistics and Probability
  • Feature Engineering and Model Evaluation
  • Deep Learning, NLP, Computer Vision (depending on the role)
  • MLOps, Docker, Kubernetes, MLflow
  • Cloud Platforms (AWS, Azure, Google Cloud Platform)
  • Spark, Databricks, Hadoop
  • Git, CI/CD pipelines
  • Data Modeling and Data Engineering concepts

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