In-Demand Machine learning engineer Jobs in Parramatta, Nsw

Senior Gemini Platform Engineer
Accenture —Sydney NSW
- Full-time
- Management
- ERP systems
- SQL
Senior Data Analytics /BI Lead
Macquarie Group Limited —Sydney NSW
- Full-time
- Hybrid work
- Master's degree
- AI
- Analytics
- Annual leave
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Systems Engineer, Region Services
Amazon Support Services Pty Ltd —Sydney NSW
- Full-time
- AWS
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Databricks Resident Solutions Architect
Ultra Tendency —Sydney NSW
- Remote
- Spanish
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- Master's degree
Quick Apply
Systems Engineer - Windows , Region Services
Amazon Support Services Pty Ltd —Sydney NSW
- Full-time
- Administrative experience
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- AI
Sr Security Engineer, Region Services
Amazon Web Services Australia Pty Ltd —Sydney NSW
- Full-time
- AWS
- Scripting
- AI
- Health insurance
- Employee assistance program
Data Engineer
Optus —Sydney NSW
- SQL
- Parental leave
- Employee discount
2h

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Senior Data Analytics /BI Lead
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machine learning engineer jobs near parramatta, nsw

Senior Gemini Platform Engineer

Accenture -
Sydney NSW

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Job Details

Full-time

Qualifications

CI/CD
Management
SAP
ERP systems
Microservices
SQL
Machine learning
APIs
Security
ServiceNow
AI
Identity & access management

Full Job Description

Accenture is a global professional services company with leading capabilities in digital, cloud and security. Find out more about us at accenture.com.

Role Focus

You will lead the technical architecture and delivery of enterprise-grade AI platforms. Your mission is to move clients from "Experimentation" to "Production" by architecting high-performance, secure, and cost-optimized environments for Gemini models. You will be responsible for architecting and implementing Google cloud, Gemini Enterprise, Vertex AI and other Google specific technology focused solutions for Accenture’s clients.

Key Responsibilities & Toolset Proficiency

Generative AI & Model Management

Gemini 1.5 Pro/Flash & Model Garden: Deploy and fine-tune Gemini models for specialized tasks (reasoning, long-context analysis, multimodal processing).

Vertex AI Studio: Manage the lifecycle of prompts and model versions, ensuring optimal performance across different enterprise use cases.

Orchestration & Agentic Logic

Vertex AI Agent Builder: Build "System-of-Action" agents using the native Google stack to minimize latency and maximize security.

Multi-Agent Systems: Architect complex, stateful agentic workflows, utilizing Google’s solutions and other relevant industry leading solutions to solve complex business use-cases spanning multiple industries.

Agent Development Kit (ADK): Standardize the creation of agents to ensure portability and consistency across the enterprise.

Data & Vector Infrastructure

BigQuery (Vector Search): Integrate structured business data with unstructured vector embeddings directly within BigQuery to power grounded, real-time AI responses.

Vertex AI Search & Conversation: Implement RAG (Retrieval-Augmented Generation) at scale, ensuring agents have access to the most recent and relevant enterprise knowledge.

Cloud Architecture & Engineering

Microservices (Cloud Run/GKE): Containerize and scale agentic applications using GKE for high-performance workloads or Cloud Run for serverless efficiency.

Event-Driven Design (Pub/Sub): Build asynchronous, resilient AI pipelines that trigger actions across the enterprise based on real-time data events.

Development & MLOps

Python & API Design: Craft robust, clean, and performant Python code and design secure APIs that connect Gemini to legacy systems (ServiceNow, SAP, Oracle).

CI/CD for ML (MLOps): Implement automated testing, deployment, and monitoring pipelines to manage model drift and ensure reliability.

Productivity Tools: Leverage Gemini Code Assist, Gemini CLI, and Antigravity to accelerate the development lifecycle and automate repetitive infrastructure tasks.

Technical Requirements

Platform Mastery: Advanced experience with the Vertex AI suite, including Model Garden and Agent Builder. Experience with Gemini CLI, Code Assist, Agent development kit and the future breadth of Google development tools.

Engineering Excellence: Proven track record of designing and deploying AI and Agentic frameworks, architecting agentic workflows compliant with responsible AI guideline. Sound knowledge of Google cloud solutions including Google Kubernetes engine and other relevant tools.

MLOps Discipline: Hands-on experience with Vertex AI Pipelines or Kubeflow for managing production AI lifecycles.

Data Savvy: Proficiency in SQL/BigQuery and vector database management.

Security Mindset: Deep understanding of Google Cloud VPC Service Controls, IAM, RAI and enterprise security protocols for AI.

Qualifications

5+ years in cloud engineering, Machine learning and AI development.

3+ years specifically focused on AI/ML implementation and deploying agentic systems

Proven track record of delivering end-to-end AI solutionsfor enterprise clients.

Why this role?

In this role, you aren't just an "AI developer"—you are a Reinvention Engineer. You are building the "Enterprise AI foundation” for our clients across business verticals. You will be the technical lead who ensures that when our clients deploy multi-agent systems, the infrastructure is as robust, secure, and performant as their core banking or ERP systems.

#LI_GM #LI-GM

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machine learning engineer jobs in parramatta, nsw

Senior Gemini Platform Engineer

Senior Data Analytics /BI Lead

Systems Engineer, Region Services

Databricks Resident Solutions Architect

Systems Engineer - Windows , Region Services

Sr Security Engineer, Region Services

Data Engineer

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