Classification: Contract
Contract Length: 12 months
Location: Nashville, TN
CereCore is seeking an AI Solutions Architect to join our team onsite in Nashville, TN.
Summary:
This role requires contributing to strategic technical direction and system architecture approaches for individual projects and platform migrations.
Responsibilities:
- Responsible for building and supporting a Cloud based ecosystem designed for enterprise-wide analysis of structured, semi-structured, and unstructured data
- Design the cloud environment data model from a comprehensive perspective, ensuring that it satisfies all the company’s needs
- Develop, manage, and own full data lifecycle from raw data acquisition through transformation to end user consumption
- Share knowledge and experience to contribute to growth of overall team capabilities
- Maintain a holistic view of information assets by creating and maintaining artifacts that illustrate how information is stored, processed, and accessed
- Demonstrate deep understanding and act as a leader in the team’s continuous integration and continuous delivery automation pipeline
- Collaborate with business analysts, project lead, management, and customers on requirements
- Design fit-for-purpose products to ensure products align to the customer's strategic plans and technology road maps
- Actively participate in technical group discussions and adopt any new technologies to improve the development and operations
- Assist team members with production issues and offer support, guidance, and assist in communicating issues with appropriate stakeholders when necessary
Requirements:
- 5+ years specifically focused on the practical implementation of AI/ ML / Automation solutions within enterprise environments
- A demonstrated history of successfully delivering high-impact AI/ML solutions from concept to production, with a clear focus on solving real-world business problems
- Extensive experience designing, building, and deploying solutions on major cloud platforms (AWS, Azure, or GCP), including leveraging their AI/ML services effectively
- Programming Languages: Mastery of core languages like Python and Java for AI/ML development and system integration
- Data Handling: Strong understanding of data structures, algorithms, and big data technologies (e.g., Hadoop, Spark, or Kafka) for efficient data processing and feature engineering
- Familiarity with DevOps and MLOps practices and tools (e.g., Git, Kubernetes, CI/CD) for streamlining the AI/ML lifecycle, including model deployment, monitoring, and maintenance.
- Experience with optimizing solutions for performance, scalability, and cost-effectiveness in production environments