Machine Learning Solutions Lead Specialist Engineer

Boulder, CO - USA

Monthly Salary: Not Disclosed

Posted on: 12 hours ago

Vacancies: 1 Vacancy

Job Summary

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

Job Summary

We are seeking an experienced Machine Learning Solutions Lead Specialist Engineer to architect and deliver advanced machine learning and AI-driven solutions across complex enterprise environments. The ideal candidate will have deep expertise in machine learning frameworks scalable model deployment cloud-based ML platforms and experience leading teams to translate business challenges into actionable ML strategies. This role involves end-to-end project ownership including problem definition data engineering collaboration model development MLOps automation and implementation of production-ready ML systems.

Key Responsibilities

Lead the design development and deployment of scalable machine learning models algorithms and advanced analytics solutions.
Work closely with business stakeholders to identify opportunities for ML-driven automation and intelligent insights.
Drive end-to-end ML lifecycle including data exploration feature engineering model training validation and production deployment.
Architect AI/ML systems using modern cloud platforms such as AWS Azure or Google Cloud.
Implement MLOps best practices for CI/CD pipelines model versioning monitoring and automated retraining.
Develop reusable ML frameworks tools and libraries to support predictive analytics and real-time decision systems.
Collaborate with data engineers data scientists and software development teams to integrate models within enterprise platforms.
Conduct performance tuning evaluation and testing of ML models to ensure accuracy reliability scalability and ethical compliance.
Mentor and provide technical leadership to junior engineers and data science team members.
Document solution architectures and present technical strategies to leadership and cross-functional teams.

Required Qualifications

12 years of experience in machine learning artificial intelligence or advanced data science roles.
Strong expertise in Python and ML libraries such as TensorFlow PyTorch Scikit-learn Keras and XGBoost.
Solid understanding of distributed computing technologies such as Spark Ray or Dask.
Hands-on experience with cloud-based ML platforms including AWS SageMaker Azure ML or GCP Vertex AI.
Proven experience building and deploying large-scale ML pipelines and production-grade AI solutions.
Strong background in statistics probability optimization techniques and feature engineering methods.
Experience with MLOps tools such as MLflow Kubeflow Airflow Docker and Kubernetes.
Strong problem-solving analytical and communication skills.

Preferred Skills

Experience working with LLMs generative AI and transformer-based architectures.
Familiarity with real-time inference systems streaming platforms and event-driven processing such as Kafka or Flink.
Experience with data governance model explainability fairness and compliance frameworks.
Knowledge of domain-specific ML applications such as forecasting recommendation engines NLP computer vision or reinforcement learning.
Previous experience in leading AI-driven transformation programs or consulting environments.
Advanced degree (Masters or Ph.D.) in Computer Science Data Science Machine Learning Mathematics or related field.

Job Title: Machine Learning Solutions Lead Specialist Engineer Location: Boulder CO Experience: 12 Years Employment Type: Contract Interview Type: In-Person or Webcam Job Summary We are seeking an experienced Machine Learning Solutions Lead Specialist Engineer to architect and deliver advanced machi...

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

Job Summary

Key Responsibilities

Lead the design development and deployment of scalable machine learning models algorithms and advanced analytics solutions.
Work closely with business stakeholders to identify opportunities for ML-driven automation and intelligent insights.
Drive end-to-end ML lifecycle including data exploration feature engineering model training validation and production deployment.
Architect AI/ML systems using modern cloud platforms such as AWS Azure or Google Cloud.
Implement MLOps best practices for CI/CD pipelines model versioning monitoring and automated retraining.
Develop reusable ML frameworks tools and libraries to support predictive analytics and real-time decision systems.
Collaborate with data engineers data scientists and software development teams to integrate models within enterprise platforms.
Conduct performance tuning evaluation and testing of ML models to ensure accuracy reliability scalability and ethical compliance.
Mentor and provide technical leadership to junior engineers and data science team members.
Document solution architectures and present technical strategies to leadership and cross-functional teams.

Required Qualifications

12 years of experience in machine learning artificial intelligence or advanced data science roles.
Strong expertise in Python and ML libraries such as TensorFlow PyTorch Scikit-learn Keras and XGBoost.
Solid understanding of distributed computing technologies such as Spark Ray or Dask.
Hands-on experience with cloud-based ML platforms including AWS SageMaker Azure ML or GCP Vertex AI.
Proven experience building and deploying large-scale ML pipelines and production-grade AI solutions.
Strong background in statistics probability optimization techniques and feature engineering methods.
Experience with MLOps tools such as MLflow Kubeflow Airflow Docker and Kubernetes.
Strong problem-solving analytical and communication skills.

Preferred Skills

Experience working with LLMs generative AI and transformer-based architectures.
Familiarity with real-time inference systems streaming platforms and event-driven processing such as Kafka or Flink.
Experience with data governance model explainability fairness and compliance frameworks.
Knowledge of domain-specific ML applications such as forecasting recommendation engines NLP computer vision or reinforcement learning.
Previous experience in leading AI-driven transformation programs or consulting environments.
Advanced degree (Masters or Ph.D.) in Computer Science Data Science Machine Learning Mathematics or related field.