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About the Role

Lead the design and delivery of agentic AI systems, focusing on both agent-facing workstreams and the underlying context layer. This foundational role involves owning pipelines, orchestration, conversational interfaces, memory management, knowledge graph integration, and retrieval infrastructure, while collaborating with cross-functional teams to shape AI solutions at scale.

Responsibilities

• Lead the architecture and delivery of agentic AI systems end-to-end, including agents, orchestration, tool use, and multi-step reasoning workflows.
• Own the context layer: design and implement memory architectures and integrate GraphRAG and knowledge graph retrieval into agentic pipelines.
• Build robust RAG systems and ensure retrieval quality through evaluation frameworks.
• Translate client requirements into technical designs, presenting approaches and trade-offs to stakeholders.
• Define standards and reusable patterns for agentic AI development.
• Set up observability, evaluation, and monitoring pipelines for AI systems 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 and vector databases.
• 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.

Skills

• Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, or similar)
• LLM APIs (OpenAI, Anthropic, etc.)
• Agent design patterns (ReAct, planning loops, tool use, multi-agent coordination, memory architectures)
• GraphRAG or knowledge graph-based retrieval
• Vector databases (Pinecone, Weaviate, Qdrant, etc.)
• Python
• Software engineering fundamentals (APIs, testing, CI/CD, containerisation)
• Docker
• Kubernetes
• Communication skills

Experience Level

• Senior

Benefits

• Work on real-world AI and advanced analytics solutions with measurable business impact.
• Collaborate with a global team of engineers and data scientists.
• Exposure to diverse industries, modern cloud platforms, and cutting-edge AI technologies.
• A collaborative culture that values real outcomes.
• Rapid learning opportunities and diverse challenges.
• Flat organisational hierarchy with high visibility and accessibility to our leaders.

About the Company

• We are investing in agentic AI.
• Lynx builds and ships AI solutions at scale.