Aventa Credit Union was chartered as Colorado Springs Employees Credit Union (CSECU) in 1957 by the Colorado Springs Utilities department. Shortly thereafter, the employees of the City of Colorado Springs also chartered CSECU as their financial partner. Through the years we've grown and expanded beyond Colorado Springs. In May 2009, Colorado Springs Credit Union changed its name to Aventa Credit Union to reflect a more diverse membership base. In March 2010, Pueblo City Employees Credit Union in Pueblo became a part of Aventa, following a vote of its membership in favor of a merger and in March 2012, the Saguache County Credit Union (SCCU) was dissolved by the National Credit Union Administration and acquired by Aventa. Today, we serve City of Colorado Springs employees, City of Pueblo employees, people who live or work in Saguache County and various other employees of our Select Employer Groups (SEGs). Currently, credit unions are among the country's strongest and most stable financial models, thanks to direct accountability to membership and conservative management philosophies. Aventa is proud to be part of the US credit union heritage. Whether our members are just starting out or are well on their way to financial success, we are committed to delivering financial solutions in unexpected ways, making a positive impact in the lives of our members and the community.
Job Summary
- We are seeking a Junior Machine Learning Engineer to develop AI agents and data-driven solutions that enhance the efficiency and performance of our operations.
- The successful candidate will be responsible for designing, training, and deploying AI agents and ML models to automate workflows, monitor operations in real time, and generate intelligent insights across various departments.
- This role will collaborate with engineering, business development, and operations teams to develop tools that solve real-world problems across complex domains.
Key Responsibilities
AI Agent Development & Automation:
- Design and build AI agents and intelligent applications to automate repetitive tasks and support decision-making.
- Develop domain-specific agents tailored for different company verticals (e.g., engineering, real estate, oil & gas, healthcare).
- Implement NLP, computer vision, and reinforcement learning where applicable to enhance agent capabilities.
Data Analysis & Real-Time Visualization:
- Collect, process, and analyze structured and unstructured data from multiple business units.
- Build dashboards and visualizations to provide real-time insights into project and operational metrics.
- Use data science methods to identify performance trends, anomalies, and opportunities for efficiency improvement.
Solution Development:
- Translate business needs into custom machine learning models or data solutions.
- Design predictive models and recommender systems to support internal project planning and risk mitigation.
- Work with software and product teams to integrate ML solutions into business tools and applications.
Collaboration & Support:
- Collaborate with project, finance, HR, and technical departments to understand operational pain points and identify opportunities for intelligent automation.
- Support company-wide digital transformation by championing AI-enabled systems and workflow redesigns.
- Contribute to training and onboarding sessions for internal users of AI tools.
Requirements
Education & Experience:
- Bachelor’s or Master’s Degree in Computer Science, Machine Learning, Data Science, Engineering, or a related field.
- 1–3 years of hands-on experience in AI or ML model development (including academic or internship projects).
- Strong portfolio of AI/ML projects — submission of previous work is mandatory.
- Show previous application of AI in real world business is mandatory.
Technical Skills:
- Applying AI applications across suite of business applications to form AI Agents with existing LLMs from ChatGPT, Perplexity, Gemini and more.
- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Experience in building and deploying machine learning models (classification, regression, clustering, etc.).
- Familiarity with APIs, web scraping, and working with both SQL and NoSQL databases.
- Hands-on experience with data visualization tools (e.g., Power BI, Tableau, Plotly, or Dash).
- Basic understanding of cloud platforms (AWS, Azure, or GCP) and CI/CD for ML deployment is a plus.
Preferred Experience:
- Experience with building AI assistants, task automation tools, or generative AI applications.
- Exposure to multi-sector operations: real estate, energy, health, infrastructure, etc.
- Familiarity with data engineering concepts and pipeline development is an added advantage.
Personal Attributes:
- Self-motivated and intellectually curious with a passion for emerging technologies.
- Strong communication and documentation skills.
- Excellent problem-solving ability and eagerness to experiment with new ideas.
- Ability to thrive in a fast-paced, multi-disciplinary environment.
Method of Application
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