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.