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Lead AI Engineer - Enterprise AI Platforms (LLMs | AWS | Databricks)
We're partnering with a large enterprise at a critical point in its AI evolution.
This role is for a Lead Engineer who can own the design, delivery and operation of production AI systems — setting technical direction while staying hands-on. You'll lead the build of AI platforms that are safe, scalable, observable and trusted, not experimental or hype-driven.
This is a true technical leadership role at the intersection of AI engineering, platform architecture, governance and cross-functional delivery.
What You'll Own
What You Bring
- Python | FastAPI | React + TypeScript
- Databricks Lakehouse | Vector DBs (pgvector or equivalent)
- Advanced RAG + guardrails
- AWS cloud platforms
- CI/CD, automated evals, model registries
- Observability (metrics, logs, traces)
This Role Suits Someone Who
Apply now or reach out for a confidential discussion.
Lead AI Engineer
| Location: | Sydney |
| Discipline: | Technology |
| Job type: | Permanent |
| Salary: | 180000 to 200000 |
| Contact name: | Nick Paisley |
| Contact email: | nickp@talentlink.com.au |
| Job ref: | 1421539 |
| Published: | about 16 hours ago |
| Startdate: | ASAP |
We're partnering with a large enterprise at a critical point in its AI evolution.
This role is for a Lead Engineer who can own the design, delivery and operation of production AI systems — setting technical direction while staying hands-on. You'll lead the build of AI platforms that are safe, scalable, observable and trusted, not experimental or hype-driven.
This is a true technical leadership role at the intersection of AI engineering, platform architecture, governance and cross-functional delivery.
What You'll Own
- Technical ownership of enterprise-grade AI systems using LLMs and agentic architectures
- Design and operation of LLMOps / AgentOps (prompt lifecycle, evaluation, red-teaming, HITL controls)
- Architecture of end-to-end AI data paths: ingestion, embeddings, vector stores, RAG pipelines, caching and quality controls
- Safe release strategies: canary deployments, A/B testing, feature flags and controlled exposure
- AI system observability, reliability and incident response
- Platform evolution, architectural standards and technical debt roadmaps
- Technical leadership across teams, including mentoring and engineering standards
- Deep collaboration with Product, Security, Legal/Privacy and Executive stakeholders
What You Bring
- 6+ years in software engineering with significant ownership of production AI systems
- Deep hands-on experience with LLMs, RAG, tool/function calling, planning and memory
- Strong delivery experience on AWS and Databricks (MLflow / Mosaic AI)
- Advanced Python engineering (FastAPI / FastMCP preferred)
- Strong cloud architecture, CI/CD and Infrastructure-as-Code capability
- Deep understanding of AI safety, evaluation, governance and risk controls
- Experience leading engineers and setting technical direction
- Python | FastAPI | React + TypeScript
- Databricks Lakehouse | Vector DBs (pgvector or equivalent)
- Advanced RAG + guardrails
- AWS cloud platforms
- CI/CD, automated evals, model registries
- Observability (metrics, logs, traces)
This Role Suits Someone Who
- Thinks in systems, not demos
- Understands AI failure modes and operational risk
- Balances speed, safety and scalability
- Can lead technically while remaining hands-on
- Is comfortable influencing across engineering, product and executive teams
Apply now or reach out for a confidential discussion.
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