Senior AI Engineer

Overview

• Architect and lead the implementation of multi-agent systems using Google AI SDKs (Vertex AI Agent Builder), Lang Graph, CrewAI, and other emerging orchestration frameworks.
• Design and build stateful, tool-augmented agents capable of advanced reasoning, long-term planning, and autonomous execution.
• Develop and document agent orchestration patterns including planner-executor, supervisor-worker, and hierarchical agent structures.
• Implement sophisticated memory systems (short-term, long-term, and cross-session contextual memory).
• Enable seamless cross-agent communication and multi-modal coordination.
• Lead the delivery of production-grade LLM applications: RAG pipelines, specialised agents, and developer copilots.
• Integrate diverse tools, enterprise APIs, and legacy systems into agentic workflows.
• Design robust system prompts, dynamic routing logic, and AI guardrails using Vertex AI Model Garden or Azure AI Studio.
• Drive optimisation of AI workflows for latency, token cost, and output quality.
• Develop and own reusable AI microservices, agent frameworks, and standardised APIs.
• Contribute to core AI platform capabilities including model routing, centralised observability, and safety filters.
• Define and enforce engineering standards and best practices for AI development across the team.
• Deploy and manage agent-based systems on GCP, Azure, and/or AWS using Docker, Kubernetes (GKE/AKS/EKS), and Cloud Run.
• Implement comprehensive monitoring and observability using Vertex AI Inspector, Lang Smith, or Azure Monitor.
• Drive incident response and post-mortems for production AI system failures.
• Act as a technical lead on key AI engineering work streams, shaping architecture and approach.
• Mentor and support more junior AI engineers through code review, design discussions, and pair programming.
• Collaborate with Principal AI Engineer and cross-functional teams (data, product, delivery) to align AI engineering with business outcomes.
• Stay at the forefront of the rapidly evolving agentic AI landscape and bring new approaches into the team.

• 5–8 years of software engineering experience with at least 3 years focused on LLM-based or AI systems in production.
• Proven track record building and shipping RAG pipelines, autonomous agents, and multi-step reasoning chains.
• Strong hands-on experience with Google AI SDKs, Vertex AI, and/or Azure AI services.
• Deep proficiency in orchestration stacks:
  Lang Graph, CrewAI, Llama Index, Haystack, or comparable frameworks.
• Expert-level Python; strong backend development skills (FastAPI, Go, or Node.js).
• Deep understanding of agent design patterns: planning, reflection, memory, and tool-use.
• Experience integrating complex enterprise APIs and event-driven systems into agentic workflows.
• Proven ability to trace, debug, and improve non-deterministic, multi-step AI reasoning pipelines.
• Strong instinct for building resilient, observable, and production-ready AI systems.
• Strong familiarity with GCP and/or Azure core services: GKE, Cloud Run, Azure AI services.
• Infrastructure as Code:
  Terraform or Pulumi.
• CI/CD: experience building automated evaluation and deployment pipelines for AI models.

Hard Skills

• Python
• FastAPI
• Go
• Node.js
• Google AI SDKs
• Vertex AI
• Azure AI services
• Lang Graph
• CrewAI
• RAG pipelines

Soft Skills

• leadership
• mentoring
• collaboration
• problem-solving
• communication
• design discussions
• code review
• incident response
• optimisation
• best practices