AI Engineer (US)

What This Involves:

• Lead the architecture and delivery of agentic AI systems end-to-end: agents, orchestration, tool use, and multi-step reasoning workflows.
• Own the context layer: design and implement memory architectures (episodic, semantic, working memory) and integrate GraphRAG and knowledge graph retrieval into agentic pipelines.
• Build robust RAG systems — including vector retrieval, graph traversal, and hybrid search — and ensure retrieval quality through evaluation frameworks.
• Translate client requirements into technical designs, presenting approaches and trade-offs to both technical and non-technical stakeholders.
• Define standards and reusable patterns for agentic AI development that other engineers at Lynx can build on.
• Set up observability, evaluation, and monitoring pipelines to ensure AI systems perform correctly in production.

Requirements:

• 5–8 years of software or ML engineering experience, with at least 2–3 years building LLM-based or agentic AI systems in production.
• Deep hands-on experience with agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, or similar) and LLM APIs (OpenAI, Anthropic, etc.).
• Strong understanding of agent design patterns: ReAct, planning loops, tool use, multi-agent coordination, and memory architectures.
• Practical experience with GraphRAG or knowledge graph-based retrieval (e.g., Neo4j, Microsoft GraphRAG) and vector databases (Pinecone, Weaviate, Qdrant, etc.).
• Proficiency in Python and solid software engineering fundamentals: APIs, testing, CI/CD, containerisation (Docker/Kubernetes).
• Experience working in a consulting or client-facing environment — comfortable presenting technical approaches and adapting to ambiguous requirements.
• Strong written and verbal communication skills across distributed, cross-functional teams.

Key Competencies:

• Stakeholder Mentality: Treats the company's and client’s goals as their own and is genuinely motivated by its success.
• Organisational Excellence: Manages time and priorities effectively, ensuring tasks are completed accurately and on time even in a fast-paced environment.
• Discretion & Integrity: Handles sensitive and confidential information with professionalism and sound judgement.
• Problem Solving: Approaches challenges proactively and with a solution-oriented mindset, taking initiative rather than waiting to be directed.
• Collaboration: A team player who builds strong working relationships and communicates effectively with colleagues across all levels.