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