Machine Learning Engineer Lead Specialist Engineer

Chicago, IL - USA

Monthly Salary: Not Disclosed

Posted on: 6 hours ago

Vacancies: 1 Vacancy

Job Summary

Job Title: Machine Learning Engineer Lead Specialist Engineer
Location: Chicago Illinois
Experience: 12 Years
Employment Type: Contract
Interview Type: In-Person or Webcam

Job Description

We are seeking a highly experienced Machine Learning Engineer Lead Specialist Engineer to oversee the design development and deployment of advanced machine learning models and AI-driven solutions. The ideal candidate will have a strong background in machine learning frameworks data science practices deep learning and large-scale production deployment. This role involves leading complex AI initiatives driving innovation collaborating with cross-functional teams and mentoring junior engineers to deliver high-quality solutions that support enterprise-level initiatives.

Key Responsibilities

Lead end-to-end development of machine learning models from data exploration and feature engineering to training validation and deployment.
Architect scalable ML pipelines and production-level model lifecycle management.
Collaborate closely with data engineering software development and product teams to integrate ML models into enterprise systems.
Evaluate experiment and implement new machine learning and deep learning methodologies and technologies.
Analyze large datasets to extract insights and identify opportunities for improvement and automation.
Perform model tuning optimization testing and performance monitoring in production environments.
Provide technical leadership mentorship and code review guidance for ML and data engineering teams.
Develop and maintain documentation standard operating procedures and best practices for ML workflows.
Ensure AI/ML solutions follow compliance security and performance standards.
Engage in research and stay current with emerging trends in AI data science and MLOps technologies.

Required Qualifications

Bachelors or Masters degree in Computer Science Data Science Artificial Intelligence Engineering or a related field.
12 years of experience in machine learning engineering AI systems development or applied data science in enterprise environments.
Strong programming expertise in Python and familiarity with libraries such as TensorFlow PyTorch Scikit-learn and Keras.
Proficiency in building and deploying ML models using cloud platforms such as AWS Azure or Google Cloud.
Strong experience with MLOps tools such as MLflow Kubeflow SageMaker or Databricks.
Expertise in data processing frameworks such as Spark Hadoop Pandas and SQL.
Proven ability to deploy models into production and manage continuous monitoring and retraining cycles.
Strong understanding of computer science fundamentals including algorithms data structures and system design.
Excellent communication leadership and stakeholder management skills.

Preferred Skills

PhD in Computer Science Machine Learning or related discipline.
Experience with generative AI LLMs NLP and reinforcement learning.
Knowledge of microservices architecture and containerization (Docker Kubernetes).
Experience working in Agile environments.
Exposure to real-time data streaming technologies such as Kafka or Flink.
Strong problem-solving skills and ability to define solutions with minimal supervision.

Job Title: Machine Learning Engineer Lead Specialist Engineer Location: Chicago Illinois Experience: 12 Years Employment Type: Contract Interview Type: In-Person or Webcam Job Description We are seeking a highly experienced Machine Learning Engineer Lead Specialist Engineer to oversee the design dev...

Job Title: Machine Learning Engineer Lead Specialist Engineer
Location: Chicago Illinois
Experience: 12 Years
Employment Type: Contract
Interview Type: In-Person or Webcam

Job Description

Key Responsibilities

Lead end-to-end development of machine learning models from data exploration and feature engineering to training validation and deployment.
Architect scalable ML pipelines and production-level model lifecycle management.
Collaborate closely with data engineering software development and product teams to integrate ML models into enterprise systems.
Evaluate experiment and implement new machine learning and deep learning methodologies and technologies.
Analyze large datasets to extract insights and identify opportunities for improvement and automation.
Perform model tuning optimization testing and performance monitoring in production environments.
Provide technical leadership mentorship and code review guidance for ML and data engineering teams.
Develop and maintain documentation standard operating procedures and best practices for ML workflows.
Ensure AI/ML solutions follow compliance security and performance standards.
Engage in research and stay current with emerging trends in AI data science and MLOps technologies.

Required Qualifications

Bachelors or Masters degree in Computer Science Data Science Artificial Intelligence Engineering or a related field.
12 years of experience in machine learning engineering AI systems development or applied data science in enterprise environments.
Strong programming expertise in Python and familiarity with libraries such as TensorFlow PyTorch Scikit-learn and Keras.
Proficiency in building and deploying ML models using cloud platforms such as AWS Azure or Google Cloud.
Strong experience with MLOps tools such as MLflow Kubeflow SageMaker or Databricks.
Expertise in data processing frameworks such as Spark Hadoop Pandas and SQL.
Proven ability to deploy models into production and manage continuous monitoring and retraining cycles.
Strong understanding of computer science fundamentals including algorithms data structures and system design.
Excellent communication leadership and stakeholder management skills.

Preferred Skills

PhD in Computer Science Machine Learning or related discipline.
Experience with generative AI LLMs NLP and reinforcement learning.
Knowledge of microservices architecture and containerization (Docker Kubernetes).
Experience working in Agile environments.
Exposure to real-time data streaming technologies such as Kafka or Flink.
Strong problem-solving skills and ability to define solutions with minimal supervision.