Bachelor's degree in Computer Science, Computer Engineering, or a related technical discipline, or equivalent experience
7+ years of professional experience in software engineering, platform engineering, internal tool development, DevOps, ML systems, or a related discipline
Extensive experience developing across the LLM application stack: prompt engineering, retrieval-augmented generation (RAG), embeddings, vector search, reranking), context management, structured outputs, and tool use
Extensive experience with agentic orchestration frameworks and interoperability protocols (MCP, ACP)
Experience evaluating, fine-tuning, securing (guardrails), and cost-optimizing LLM systems in production
Strong proficiency in Python, TypeScript/JavaScript, or another modern language used for backend services and automation
Experience designing, implementing, and consuming REST APIs and backend services
Experience with Git, CI/CD, Linux/Unix environments, containers, and production deployment workflows
Experience building AI solutions that securely leverage sensitive internal data and company knowledge sources
Excellent written and verbal technical communication skills, including requirements capture and technical documentation
Ability to work across teams, clarify ambiguous problems, and turn business workflows into practical technical solutions
Self-motivated, proactive, flexible, curious, and committed to continuous learning
Experience with AWS, Kubernetes, Docker, Terraform, or other cloud and infrastructure tools
Experience integrating with Slack, Jira, GitHub, Confluence, Google Workspace, Salesforce, Zendesk, ServiceNow, or similar systems
Experience with self-hosted, open-source, or private AI model deployments
Experience in quantum computing, scientific computing, hardware/software systems, or other highly technical domains