CIP Architect

• Bachelor’s degree in Computer Science, Data Science, Software Engineering, Information Systems, Electrical Engineering, or a related technical field. \n

• 7+ years of experience in data engineering, software development, cloud architecture, AI/ML, or related disciplines. \n

• 3+ years designing enterprise data or solution architectures. \n

• Hands-on experience with AWS and cloud-native solution development. \n

• Strong knowledge of relational databases, NoSQL, data lakes, data warehouses, ETL/ELT, APIs, messaging, and event-driven architectures. \n

• Experience developing or deploying AI/ML, generative AI, or knowledge-based solutions. \n

• Knowledge of computer vision and image-processing workflows. \n

• Experience implementing CI/CD and DevSecOps controls. \n

• Proficiency in Python and SQL. \n

• Experience with Git, automated testing, and code-review practices. \n

• Strong understanding of IAM, encryption, access controls, audit logging, and secure handling of sensitive data. \n, * Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field. \n

• Experience in manufacturing, automotive, engineering, industrial, or product-development environments. \n

• Experience with AWS S3, RDS, Redshift, Glue, Lambda, API Gateway, SageMaker, Bedrock, IAM, CloudWatch, and EKS. \n

• Experience with knowledge graphs, enterprise search, vector databases, semantic data models, MLOps, or edge AI.

nLER TechForce is seeking a CIP Architect to work with our client in Columbus, Indiana. This role combines enterprise data architecture, AI governance, generative AI, computer vision, and DevSecOps, with a strong focus on building secure, scalable, production-ready solutions.\n \nWhat you’ll do\n \n \n \n

• Define enterprise data architecture, integration, storage, governance, and security strategies. \n

• Architect scalable solutions across databases, data lakes, APIs, analytics platforms, microservices, and reusable data services. \n

• Establish data quality, lineage, ownership, retention, access-control, and governance standards. \n

• Build strategies for securely making trusted enterprise data available to generative AI applications and AI agents. \n

• Define responsible AI controls for security, privacy, traceability, model risk, and human oversight. \n

• Design RAG, semantic search, vector database, knowledge repository, and AI-agent solutions. \n

• Take AI solutions from proof of concept through secure, scalable production deployment. \n

• Architect computer vision solutions for object detection, classification, OCR, defect identification, visual inspection, and image processing. \n

• Build and optimize CI/CD and DevSecOps pipelines for applications, infrastructure, databases, and AI models. \n

• Implement secure coding, vulnerability scanning, secrets management, IAM, encryption, audit logging, monitoring, and operational controls. \n

• Partner across architecture, cybersecurity, engineering, IT, product, and business teams to turn requirements into scalable technical solutions. \n

\n \nWhat you’ll need\n