Forward Deployed Engineer, Agentic Platform

Job Summary

Shape how enterprises use AI in real-world applications as a bridge between Tkxel’s North product and client engineering teams. Design, build, and deploy agentic workflows powered by Large Language Models to solve complex problems and integrate AI securely into critical sectors including finance, healthcare, and telecommunications.

Responsibilities

• Work with enterprise customers to translate business problems into well-framed agentic workflows with clear success criteria and evaluation methodologies
• Lead the design, build, and delivery of LLM-powered agents that reason, plan, and act across tools, APIs, and sensitive enterprise data sources with enterprise-grade reliability
• Build and ship features for North, the AI workspace platform, across the full product lifecycle from conceptualization through production
• Own the scoping and shaping of use cases end-to-end, working across technical areas including frontend as needed to drive effective solutions
• Contribute to shared frameworks and patterns that enable consistent, high-quality delivery across customers and teams
• Drive clarity in ambiguous situations, build alignment, and raise engineering quality across the organization
• Travel up to 20–40% to work on-site with customers and partners

Must haves

• Hands-on experience building and deploying production-grade software in Python with clean, testable, observable, and scalable code
• Experience building and deploying highly performant RAG and agentic applications, including agents that plan and execute multi-step tasks using patterns like ReAct or Plan-and-Execute
• Deep familiarity with the LLM stack: frontier models, vector databases, and orchestration frameworks
• Proven ability to build robust evaluation frameworks to measure agent accuracy, safety, and latency
• Experience working directly with customers and leading technical discussions with enterprise stakeholders to translate business needs into technical specs
• Experience owning the full scope of a use case end-to-end

Nice to haves

• Experience setting architectural standards for AI and agentic systems across distributed teams
• Experience flexing into unfamiliar technical areas such as frontend when the problem calls for it
• Exposure to regulated or sensitive industry environments including finance, healthcare, or telecoms
• Experience with enterprise security, compliance, or auditability requirements for AI systems

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