Artificial Intelligence/Machine Learning Engineer (Active Secret Clearance Required)

Key Skills

Continuous Integration & Continuous Delivery - CI/CD, Kubeflow, MLflow, MLOps tools, Natural Language Processing (NLP), NumPy, Pandas, Python Programming, PyTorch, TensorFlow

Job Description

About the Organization

Now is a great time to join Redhorse Corporation. Redhorse specializes in developing and implementing creative strategies and solutions with private, state, and federal customers in the areas of cultural and environmental resources services, climate and energy change, information technology, and intelligence services. We are hiring creative, motivated, and talented people with a passion for doing what's right, what's smart, and what works.

Position Description

Redhorse Corporation is hiring an AI/Machine Learning Engineer to expand its team for the program of record CBM+ effort with the US Air Force. We are seeking a dynamic and versatile candidate to join our team focused on predictive analysis, natural language processing (NLP), and exploratory data analysis. The ideal candidate will possess a unique combination of strong technical skills in data science, ML Ops, and data engineering, along with a proactive and self-driven attitude to thrive in a dynamic and innovative environment.

Key Responsibilities:

• Collaborate with the data science team to develop predictive models, conduct NLP research, and perform exploratory data analysis to extract valuable insights.
• Maintain and guide the development of common libraries and tools for multiple teams.
• Implement end-to-end data pipelines, from data collection and preprocessing to model deployment, ensuring efficient data engineering practices.
• Champion ML Ops and DevOps principles to streamline deployment, monitoring, and maintenance of data applications and machine learning models.
• Drive automation initiatives to enhance the efficiency of repetitive tasks and improve overall workflow.
• Utilize version control for tracking changes to code, data, and model artifacts, enabling seamless collaboration and reproducibility.
• Take ownership of projects and tasks, requiring minimal supervision, and demonstrate the ability to make independent decisions.
• Stay updated on AI trends, design patterns, and systems architecture.

Minimum Requirements:

• Bachelor's in Computer Science, Data Science, or a related field. Advanced degree preferred. Equivalent work experience will also be considered.
• Proven expertise in predictive analysis, machine learning, and NLP techniques, with a track record of implementing successful data-driven solutions.
• Proficiency in programming languages such as Python and R, along with experience working with data manipulation libraries (e.g., Pandas, NumPy) and machine learning frameworks (e.g., TensorFlow, PyTorch).
• Strong understanding of software engineering practices in a data science context, including testing, code review, and collaborative development.
• Familiarity with automation tools and practices to enhance workflow efficiency.
• Proficient in version control using Git, with the ability to collaborate effectively within a team of data scientists and engineers.
• Excellent problem-solving skills, with the ability to think critically and independently find solutions to complex technical challenges.
• Self-driven mindset with a proactive approach to taking on new challenges and responsibilities.
• Strong communication skills to effectively convey technical concepts and insights to both technical and non-technical stakeholders.

Preferred Qualifications:

• Proficiency in implementing model monitoring and management processes to ensure model performance and reliability in production.
• Experience with ML Ops practices, including model versioning, containerization, orchestration, and continuous integration/continuous deployment (CI/CD) pipelines.
• Familiarity with tools like Kubeflow, MLflow, or TFX for managing end-to-end machine learning workflows.
• Strong grasp of data engineering principles and experience with designing and optimizing data pipelines for large-scale data processing.