Supernal
Technology
SeniorAIEngineer(Clients)
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“Senior AI Engineer (Clients) at Supernal. Skills: AI agentic architectures, LLM orchestration, RAG, conversational AI, system design. Design, build, and maintain production-grade backend systems, including services, data models, and CRUD applications supporting AI-driven workflows. Develop and integrate external APIs, webhooks, and third-party systems. Build and deploy conversational and agentic AI systems. Own end-to-end technical delivery for client implementations. Translate customer requirem”
What You'll Achieve.
Own end-to-end technical delivery for client implementations, from system architecture and planning through to production deployment. Ability to own delivery outcomes end-to-end, balancing technical execution with client success and timeline management.
Industry & Context.
system design; distributed systems; clean architecture principles; workflow decomposition; failure recovery
Comfortable working in fast-paced, ambiguous environments with high ownership and minimal structure.
What They're Looking For.
Must Have
4+ years of experience in software engineering, systems engineering, or automation-focused roles delivering production-grade systems. Hands-on experience building agentic architectures, conversational AI systems, or workflow automation platforms. Proven experience working with LLM orchestration, prompt engineering, function calling, and retrieval-augmented generation (RAG). Experience building and deploying voice or real-time systems, including handling latency, streaming, and failure recovery scenarios. Familiarity with integration patterns involving APIs, webhooks, and external services in production environments. Debugging skills with the ability to diagnose complex issues across distributed systems and AI pipelines. Solid engineering fundamentals, including testing, error handling, system design, and clean architecture principles. Ability to own delivery outcomes end-to-end, balancing technical execution with client success and timeline management. Communication skills in English, with the ability to explain technical trade-offs clearly to both technical and non-technical stakeholders. Comfortable working in fast-paced, ambiguous environments with high ownership and minimal structure.
What You'll Do.
Design, build, and maintain production-grade backend systems, including services, data models, and CRUD applications supporting AI-driven workflows.
Develop and integrate external APIs, webhooks, and third-party systems.
Build and deploy conversational and agentic AI systems.
Own end-to-end technical delivery for client implementations.
Translate customer requirements into technical designs, plans, and roadmaps.
Make architectural decisions across system design, LLM orchestration, RAG pipelines, integrations, and workflow decomposition.
Debug complex production issues.
Define and enforce engineering best practices.
Collaborate closely with stakeholders to manage scope, timelines, and technical trade-offs.
Write and maintain automated tests.
How You'll Work.
Team & Collaboration
Collaborate closely with delivery and product stakeholders to manage scope, timelines, and technical trade-offs.
Communication Scope
English
Process & Methodology
timeline management
Full Job Description
## Accountabilities Design, build, and maintain production-grade backend systems, including services, data models, and CRUD applications supporting AI-driven workflows. Develop and integrate external APIs, webhooks, and third-party systems to enable secure and reliable AI agent actions. Build and deploy conversational and agentic AI systems, including multi-turn dialogue management, state handling, and tool use orchestration. Own end-to-end technical delivery for client implementations, from system architecture and planning through to production deployment. Translate customer requirements and statements of work into clear technical designs, implementation plans, and execution roadmaps. Make architectural decisions across system design, LLM orchestration, RAG pipelines, integrations, and workflow decomposition. Debug complex production issues across distributed systems, AI agents, prompts, and external dependencies while ensuring system reliability. Define and enforce engineering best practices, including testing strategies, error handling, observability, and maintainability standards. Collaborate closely with delivery and product stakeholders to manage scope, timelines, and technical trade-offs. Write and maintain automated tests (unit, integration, and end-to-end) to ensure system stability and production readiness. Requirements: 4+ years of experience in software engineering, systems engineering, or automation-focused roles delivering production-grade systems. Strong hands-on experience building agentic architectures, conversational AI systems, or workflow automation platforms. Proven experience working with LLM orchestration, prompt engineering, function calling, and retrieval-augmented generation (RAG). Experience building and deploying voice or real-time systems, including handling latency, streaming, and failure recovery scenarios. Familiarity with integration patterns involving APIs, webhooks, and external services in production environments. Strong debugging
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