Partner with architects, business stakeholders, subject matter experts, and product teams to translate business needs into practical technical solutions.
Design and operationalize generative AI, agentic AI, semantic layers, and retrieval-augmented generation (RAG) capabilities.
Evaluate technical tradeoffs and make sound engineering decisions that balance delivery speed, risk, maintainability, and long-term value.
Provide technical leadership through architecture discussions, code reviews, design reviews, and mentoring of other engineers.
Continuously explore emerging technologies, AI models, frameworks, and engineering practices to improve solution outcomes and business impact.
Who You Are (Basic Qualifications)
Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience.
5+ years of professional software engineering experience.
At least 1 year of hands-on experience developing AI-enabled or Generative AI solutions.
Experience designing, developing, deploying, and supporting production-grade software, data, and AI solutions.
Experience leading technical design or implementation efforts across complex, cross-functional initiatives.
Strong programming experience using Python and SQL in production environments.
Experience with source control, automated testing, CI/CD pipelines, observability, and modern software engineering practices.
Experience building cloud-native solutions on AWS, including serverless, event-driven, and containerized architectures.
Ability to communicate complex technical concepts and architectural decisions to both technical and non-technical stakeholders.
Experience mentoring and coaching engineers through technical guidance and collaborative development practices.
What Will Put You Ahead
Experience building AI-enabled solutions using large language models (LLMs), embeddings, graph databases, graph query languages, semantic technologies, and RAG architectures.
Experience with MCP or similar agent integration standards.
Experience designing verification and evaluation frameworks for AI-generated outputs, including testing, monitoring, observability, and governance controls.
Experience designing distributed systems using microservices, event-driven architecture, API-first design, and modular architecture patterns.
Experience applying DevSecOps and production engineering practices, including infrastructure as code, automated security controls, secrets management, feature flags, and progressive deployment strategies.
Experience with enterprise data and AI platforms such as Microsoft Fabric, Fabric IQ, Snowflake, Azure AI Foundry, or Databricks.
Experience designing and deploying agentic AI solutions for conversational or action-oriented use cases.
Experience working with reusable platforms, enterprise-scale data products, and AI-enabled business solutions.
Master's degree in Computer Science, Engineering, or a related technical discipline.
At Koch companies, we are entrepreneurs. This means we openly challenge the status quo, find new ways to create value and get rewarded for our individual contributions. Any compensation range provided for a role is an estimate determined by available market data. The actual amount may be higher or lower than the range provided considering each candidate's knowledge, skills, abilities, and geographic location. If you have questions, please speak to your recruiter about the flexibility and detail of our compensation philosophy.
Who We Are
At Koch, employees are empowered to do what they do best to make life better. Learn how our business philosophy helps employees unleash their potential while creating value for themselves and the company.
Additionally, everyone has individual work and personal needs. We seek to enable the best work environment that helps you and the business work together to produce superior results.
