AI Architect

About the role

The IT capability at Koch Engineered Solutions (KES) is a critical component of our strategy to enhance business performance through the application of technology and to transform our business profitably. Our team operates with a startup mentality, integrating closely with Engineering, Operations, Commercial, and Finance teams to design, build, and scale innovative solutions that transform KES work processes.

Responsibilities

• Partner with Product and business teams to identify high-value AI and Generative AI use cases, then lead the technical solutioning process to build the detailed architecture for how AI will solve the problem.
• Leverage expertise across multiple technical domains as part of the solutioning process (Machine Learning, GenAI/LLMs, Data Engineering, Security, Cloud Infrastructure, Application Integration, etc.).
• Experimentation - Identify, prototype, and evaluate emerging AI models, agentic frameworks, and tooling to solve problems new to the organization.
• Execute on initiatives as appropriate.

Requirements

• Experience designing and deploying AI/ML and Generative AI solutions on modern cloud platforms (Azure AI / Azure OpenAI, AWS Bedrock/SageMaker, Claude Code / Claude Cowork, or comparable).
• Proven experience communicating AI and technology concepts in both execution detail and broad terms to a variety of technical and business audiences.
• Demonstrated capability partnering with customers to understand business and operating models and translate them into AI-enabled solution proposals.

Qualifications

• 5+ years’ experience in Information Technology, including hands-on delivery of data, analytics, or AI/ML solutions.
• Experience supporting discrete engineering & manufacturing.
• Experience designing and deploying agentic AI – conversational and action-oriented AI agent platforms – to solve business problems.
• Deep understanding of Generative AI and large language model architecture (transformer models, fine-tuning, RAG, embeddings, and prompt/context engineering).
• Experience operationalizing AI – LLMOps/MLOps practices for model deployment, evaluation, prompt and version management, monitoring, and drift detection.
• Experience with vector databases and retrieval systems (e.g., Azure AI Search, pgvector, Pinecone).
• Experience with AI orchestration and agent frameworks (LangChain, LlamaIndex, Semantic Kernel, AutoGen, or similar).
• Familiarity with Responsible AI and AI governance – safety, guardrails, bias mitigation, data privacy, and emerging regulatory requirements.
• Experience with data architecture that supports AI (data lakes, graph/knowledge-graph technology, feature stores, and data pipelines).
• Technical experience with serverless and event-driven cloud services, microservices, and containers (e.g., Lambda/Azure Functions, SQS/SNS, EventBridge, API Gateway, Docker, Kubernetes, AKS/ECS).
• Experience with DevOps and MLOps methodologies and tools (CI/CD, GitHub, Azure DevOps).

Skills

• Experience in supporting discrete engineering & manufacturing.
• Experience designing and deploying agentic AI – conversational and action-oriented AI agent platforms – to solve business problems.
• Deep understanding of Generative AI and large language model architecture (transformer models, fine-tuning, RAG, embeddings, and prompt/context engineering).
• Experience operationalizing AI – LLMOps/MLOps practices for model deployment, evaluation, prompt and version management, monitoring, and drift detection.
• Experience with vector databases and retrieval systems (e.g., Azure AI Search, pgvector, Pinecone).
• Experience with AI orchestration and agent frameworks (LangChain, LlamaIndex, Semantic Kernel, AutoGen, or similar).
• Familiarity with Responsible AI and AI governance – safety, guardrails, bias mitigation, data privacy, and emerging regulatory requirements.
• Experience with data architecture that supports AI (data lakes, graph/knowledge-graph technology, feature stores, and data pipelines).
• Technical experience with serverless and event-driven cloud services, microservices, and containers (e.g., Lambda/Azure Functions, SQS/SNS, EventBridge, API Gateway, Docker, Kubernetes, AKS/ECS).
• Experience with DevOps and MLOps methodologies and tools (CI/CD, GitHub, Azure DevOps).

Benefits

• Medical
• Dental
• Vision
• Flexible Spending and Health Savings Accounts
• Life Insurance
• AD&D
• Disability
• Retail
• ADD
• Paid Vacation/Time Off
• Education Assistance
• Infertility Assistance
• Paid Parental Leave
• Adoption Assistance

Pay

Compensation details are confidential and will be shared during the interview process.

Schedule

Work schedule details are confidential and will be shared during the interview process.