atQor is hiring an AI Technical Lead to architect and build the next generation of AI agents and intelligent solutions for our customers. This is a hands-on technical leadership role — you will design, code, review, and ship production-grade agentic systems while setting the technical bar for the broader AI engineering team.
You will be the deepest AI technical voice in the room — in customer architecture sessions, internal design reviews, and the codebase itself.
What You'll Do
- Design and build AI agents — single-agent, multi-agent, and human-in-the-loop systems for enterprise use cases.
- Architect end-to-end GenAI solutions — RAG, agentic RAG, tool-using agents, structured outputs, evaluation pipelines, and guardrails.
- Own the technical reference architecture for AI engagements — model selection, orchestration framework, memory, retrieval, observability, and cost optimization.
- Write production code. Lead by example in the IDE — agent loops, tool definitions, prompt scaffolding, evaluation harnesses, and integration layers.
- Lead code and design reviews across the AI team; raise the bar on quality, testability, and reliability.
- Drive prompt engineering and evaluation as engineering disciplines — versioning, regression testing, offline/online eval, and quality metrics.
- Mentor AI engineers and developers transitioning into agentic and GenAI work.
- Partner with pre-sales on solution design, POCs, and technical demos for strategic deals.
- Stay on the frontier — evaluate new models, frameworks, and patterns; bring the right ones into atQor's stack.
What You Bring
- 8–12 years in software engineering, with at least 3 years building production GenAI / agentic systems.
- Deep hands-on experience — this is not a managerial role; you will be in code reviews and on the keyboard.
- Strong system design skills — can take an ambiguous customer problem to a working agent in days, not months.
- Excellent technical communication — can explain a multi-agent architecture to a CTO and a junior engineer in the same meeting.
- Bias for shipping — comfortable with the trade-offs of getting reliable AI into production.
Education & Certifications
- Bachelor's or Master's in Computer Science, Engineering, or related field.
- Certifications preferred: Azure AI Engineer Associate, Azure Solutions Architect Expert, Databricks ML Professional.
