[Hiring] AI Engineer @QED

Role Description

This position is responsible for designing, building, and maintaining the AI systems: the LangGraph agents, retrieval-augmented generation (RAG), multi-provider LLM orchestration, and prompt/evaluation machinery that turn customer conversations and uploaded documents into grounded, auditable cost estimates.

Good leadership skills and ability to work in US time zones as required, are both pre-requisites.

Focus Area(s) for Impact:

• Retrieval grounding & estimate/sizing accuracy
• Agent reliability, latency, and LLM cost-efficiency

Why choose us?

• Engineering at QED is driven by curiosity, responsibility, and real constraints.
• We work on real-world problems that demand clear thinking, strong engineering judgement, and ownership from start to finish.
• We focus on building systems that need to work in production and stand the test of time.
• Our projects span demanding international environments and are built using disciplined engineering practices.
• We've built an environment that promotes collaboration, trust, quality and strong systems rather than individual heroics.
• Clear, factual communication matters to us because clarity is kind.
• Everyone has real influence over architecture, technical direction and standards.
• We support flexible ways of working, long term development and prioritise sustainability.

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Data Science, or Mathematics (Required)
• Master's degree in Computer Science, Machine Learning, or AI; relevant cloud or machine-learning certification (Desirable)

Requirements

• Minimum of 6 years of progressive software engineering experience, including 1+ years building production LLM-powered or machine-learning systems (agents, RAG, prompt/evaluation pipelines).
• Proven delivery in a fast-paced environment and availability to overlap U.S./Eastern-Time business hours for client meetings (Required).
• Track record of shipping AI features to production quickly with measurable quality, latency, or cost improvements, and of working directly with clients (Desirable).

Benefits

• Flexible ways of working
• Long term development opportunities
• Prioritisation of sustainability

Key Accountabilities & Responsibilities

• LLM Agents & Orchestration: Build and maintain LangGraph agents: ReAct retrieval sub-agents, custom middleware (call-limit, completion signaling), Pydantic-typed graph state, and streaming flows.
• Metrics / Measure of Success: Agent task success rate.
• Retrieval-Augmented Generation: Own retrieval quality end to end: agentic retrieval with collection routing, LLM-driven metadata filters and similarity thresholds, query rewriting, semantic chunking, and citation tracking.
• Metrics / Measure of Success: Retrieval precision/recall on the eval set; grounding/citation accuracy. Groundedness score on the standard eval set.
• LLM Provider Integration: Providers behind the abstract interface: AWS Bedrock, OpenAI, self-hosted vLLM.
• Backend Services & Data: The platform's API services, relational and domain-knowledge data stores, the vector store, and observability/tracing for AI workflows.
• Technical Leadership & Collaboration: Set technical direction for the AI workstream; review designs and code, mentor engineers, and align with product stakeholders. Break the status quo.
• Metrics / Measure of Success: Delivery against roadmap; quality of reviews and design decisions; growth and unblock rate of mentored engineers; stakeholder satisfaction.

Required Competencies and Skills

• Technical Proficiency: Expert in production software engineering and in building LLM-powered systems: agents/orchestration, retrieval-augmented generation, and prompt/evaluation pipelines; fluent in using AI development tools to accelerate delivery.
• Functional Expertise: Deep understanding of applied AI engineering: retrieval and grounding, embeddings and vector search, multi-model integration with cost/latency tuning, and evaluation-driven iteration.
• Problem-Solving: Ability to analyse complex, non-deterministic AI behaviour from real production traces and deliver scalable, well-tested solutions fast under time pressure.
• Communication: Exceptional ability to communicate technical and model-behaviour concepts clearly to non-technical and client stakeholders.
• Judgment: Proven ability to make critical, time-sensitive decisions with limited information.

Optional / Nice to Have Skills

• Technical Proficiency: Self-hosted model inference; observability and tracing tooling for AI workflows; clean, layered service architecture.
• Functional Expertise: Cloud-platform depth; agent-reliability patterns (call caps, guardrails, convergence control).
• Problem-Solving: Experience designing evaluation/benchmark frameworks and regression fixtures that hold AI quality steady over time.
• Communication: Experience working directly with U.S.-based clients; mentoring engineers, leading design reviews, and representing AI work to clients and leadership.
• Judgment: Experience setting technical direction and making roadmap trade-offs for an AI workstream or team.