JPS Tech Solutions
Machine Learning Solutions Lead Specialist Engineer
JPS Tech Solutions, Boulder, Colorado, United States, 80301
Machine Learning Solutions Lead Specialist Engineer
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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 (Master's or Ph.D.) in Computer Science, Data Science, Machine Learning, Mathematics, or related field.
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Get AI-powered advice on this job and more exclusive features.
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 (Master's or Ph.D.) in Computer Science, Data Science, Machine Learning, Mathematics, or related field.
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