Machine Learning/AI Engineer

Element Solutions

1d•Remote

About The Position

We are seeking a highly skilled and motivated Machine Learning / AI Engineer to support a federal government program focused on designing, developing, and deploying production-grade artificial intelligence and machine learning solutions. The role will focus on building scalable, secure, and high-performing AI models that support mission-critical decision-making and operational efficiency. The Machine Learning/AI Engineer will work across the full model lifecycle—from data preparation and feature engineering to model training, deployment, and monitoring—in a regulated government environment.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics, or related field (or equivalent experience).
3+ years of experience in machine learning, data science, or AI engineering roles.
Proven experience delivering production-grade machine learning models in real-world environments.
Expert proficiency in Python and ML frameworks (scikit-learn, TensorFlow, PyTorch).
Advanced SQL development experience, including complex queries, performance tuning, and data transformation logic.
Experience leveraging Large Language Models (LLMs) in development.
Understanding of core concepts such as context windows, prompt design, and input/output structures, with the ability to apply AI tools effectively in building and enhancing solutions.
Experience building and deploying models using scalable serving frameworks (e.g., REST APIs, containerized deployments, or cloud-based inference services).
Experience working with large-scale structured and unstructured datasets.
Strong understanding of machine learning concepts including supervised/unsupervised learning, deep learning, model evaluation, and feature engineering.
Experience working in the federal government or other highly regulated environments with security and compliance requirements.
Strong analytical and problem-solving skills.
Ability to communicate complex technical concepts to non-technical stakeholders.
Strong collaboration skills across engineering, data, and product teams.
Ability to work independently in a fast-paced, mission-driven environment.
High attention to detail with a focus on model reliability and production readiness.
US Citizenship or Permanent Residency required.
Must reside in the Continental US.
Depending on the government agency, specific requirements may include public trust background check or security clearance.

Nice To Haves

Experience deploying models in cloud environments (AWS, Azure, or GCP).
Familiarity with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).
Experience with streaming or real-time inference systems.
Background in predictive maintenance, anomaly detection, or operational analytics use cases.
Familiarity with Docker, Kubernetes, and CI/CD pipelines for machine learning systems.
Experience supporting healthcare (e.g., CMS), or other federal mission systems.

Responsibilities

Develop and deploy at least three (3) production-grade AI/ML models, including use cases such as predictive maintenance, anomaly detection, classification, forecasting, or optimization.
Design, build, and maintain end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment.
Apply deep expertise in PyTorch and/or TensorFlow to develop and fine-tune advanced machine learning and deep learning models.
Implement and support scalable model serving architectures, ensuring high availability, low latency, and secure inference in production environments.
Collaborate with data engineers to access, transform, and prepare large-scale datasets for model training and inference.
Partner with product owners, analysts, and stakeholders to translate business requirements into machine learning solutions.
Monitor model performance in production, including drift detection, accuracy tracking, and retraining strategies.
Ensure compliance with federal security, privacy, and governance standards in all AI/ML implementations.
Participate in Agile development cycles, including sprint planning, design reviews, and technical demonstrations.
Document model architectures, training methodologies, and deployment processes for maintainability and auditability.