Overview
- Architect and lead the implementation of multi-agent systems using Google AI SDKs (Vertex AI Agent Builder), LangGraph, 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, LangSmith, or Azure Monitor.
- Drive incident response and post-mortems for production AI system failures.
- Act as a technical lead on key AI engineering workstreams, 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.
Requirements
- 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: LangGraph, CrewAI, LlamaIndex, 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.
ATS Optimization Keywords
Hard Skills
- Python
- FastAPI
- Go
- Node.js
- Google AI SDKs
- Vertex AI
- Azure AI services
- LangGraph
- CrewAI
- RAG pipelines
Soft Skills
- leadership
- mentoring
- collaboration
- problem-solving
- communication
- design discussions
- code review
- incident response
- optimisation
- best practices