We are seeking a Lead Data & AI Platform Engineer to design, build, and operate the platforms that support data engineering and AI delivery across a range of client engagements. You will lead platform automation, deployment, governance, observability, and reliability for data pipelines, machine learning systems, LLM applications, and AI agents.

This is a hands-on technical leadership role. You will guide architectural decisions, establish reusable engineering practices, and work closely with data and AI teams to bring solutions into production.

Responsibilities

- Lead the design and implementation of secure, scalable cloud infrastructure for data and AI workloads
- Build reusable infrastructure and CI/CD patterns for data platforms, applications, models, and AI agents
- Establish operational practices for AI solutions, including deployment, versioning, evaluation, monitoring, and rollback
- Implement observability across pipelines and AI applications, including logs, metrics, traces, alerts, quality measures, and cost monitoring
- Improve platform reliability through automation, incident response, performance tuning, and capacity planning
- Define standards for cloud security, identity and access management, secrets, networking, and governance
- Support teams deploying LLM applications and agentic systems, including their model integrations, tool calls, and external APIs
- Use AI-assisted tools to improve infrastructure development, troubleshooting, and operational workflows, while validating their output
- Partner with architects, engineers, and stakeholders to make platform decisions and explain technical trade-offs
- Mentor engineers and contribute to technical standards, reusable templates, and platform roadmaps

Requirements

- Solid hands-on experience in platform engineering, DevOps, site reliability engineering, or MLOps,



with experience leading technical delivery
- Experience with at least one major cloud platform: Azure, AWS, or Google Cloud
- Strong infrastructure as code skills, such as Terraform
- Experience designing CI/CD pipelines and automated deployment processes
- Scripting or programming experience, preferably in Python
- Experience operating data platforms, distributed workloads, or cloud-native applications
- Strong understanding of monitoring, logging, alerting, incident response, and production troubleshooting
- Knowledge of cloud networking, identity and access management, secrets management, and security practices
- Practical understanding of AI/ML platform operations, including model deployment, versioning, monitoring, and lifecycle management
- Understanding of the operational needs of LLM applications and AI agents, including tracing, evaluation, latency, reliability, and cost
- Ability to lead technical decisions, mentor engineers, and collaborate directly with client and delivery teams

Nice to have

- Experience with Databricks, Snowflake, Microsoft Fabric, or similar data and AI platforms
- Experience with MLflow, model serving platforms, or model registries
- Experience deploying and observing RAG applications or agentic systems
- Familiarity with LLM evaluation, guardrails, and monitoring for response quality
- Experience with Kubernetes, containers, API gateways, or service orchestration
- Experience applying AI to operations, such as alert correlation, incident triage, or root cause analysis
- Experience with GitHub Actions, Azure DevOps, GitLab CI, or similar tools
- Experience with Databricks Asset Bundles, Unity Catalog, or equivalent capabilities
- FinOps experience, including workload optimisation and cloud cost attribution
- Consulting experience, including platform assessments, solution design, and technical estimation

📌 Lead Data & AI Platform Engineer (Melbourne)
🏢 EPAM Systems
📍 Melbourne