Machine Learning Lead

BULLIT MANAGEMENT SERVICES LIMITED; Sydney; On-site; AUD 180,000 - 240,000; Full time

Job summary

BULLIT MANAGEMENT SERVICES LIMITED is seeking a senior AI Platform Engineer to lead GenAI operations and scalable ML platforms across cloud and hybrid environments.

You will design production-grade deployment architectures, build CI/CD/CT pipelines for AI workloads, and manage data lineage, monitoring, and governance across enterprise AI pipelines.

Ideal candidate has 10+ years IT and 5+ years in ML engineering/leadership, with Docker, Kubernetes, and modern MLOps tools experience.

Qualifications

• 10+ years overall IT experience
• 5+ years in Machine Learning Engineering, MLOps, AI Platform Engineering, or AI Operations leadership

Responsibilities

• Design and implement production-grade model deployment architectures.
• Develop CI/CD and CT pipelines for AI and ML workloads.
• Automate model packaging, testing, validation, deployment, and rollback processes.
• Enable scalable serving infrastructure for ML and GenAI applications.
• Support batch, real-time, and streaming inference architectures.
• Build and manage enterprise AI platforms on cloud and hybrid environments.
• Implement containerized AI workloads using Docker and Kubernetes.
• Establish scalable GPU-enabled environments for model training and inference.
• Monitor model accuracy, drift, bias, performance, latency, and usage.
• Establish alerting and automated remediation mechanisms.
• Develop AI observability dashboards and operational metrics.
• Lead root cause analysis for AI platform and model performance issues.
• Establish data lineage and model traceability mechanisms.
• Define data quality controls and validation standards.
• Govern training, validation, and inference datasets.
• Ensure reproducibility and auditability across AI pipelines.
• Operationalize GenAI and LLM-based solutions.
• Build deployment frameworks for RAG, Agentic AI, and LLM applications.
• Manage prompt versioning, evaluation frameworks, and model governance.
• Define monitoring controls for LLM performance, hallucination rates, and response quality.
• Support model orchestration frameworks including LangChain, LangGraph, CrewAI, and Vertex AI Agents.
• Collaborate with business leaders, AI teams, operations teams, and executive stakeholders.
• Translate business requirements into scalable AI operational solutions.
• Present AI operational status and platform roadmap updates to leadership.
• Manage vendor and cloud provider engagements.

Skills

MLOps Platforms

DevOps & Automation

Python

SQL

BigQuery

Snowflake

Spark

Airflow

GenAI Platforms

Tools

Jenkins

GitHub Actions

GitLab CI/CD

Docker

Kubernetes

Terraform

CloudFormation

Ansible

Job description

10+ years overall IT experience, including 5+ years in Machine Learning Engineering, MLOps, AI Platform Engineering, or AI Operations leadership

Key Responsibilities

• Design and implement production-grade model deployment architectures.
• Develop CI/CD and CT (Continuous Training) pipelines for AI and ML workloads.
• Automate model packaging, testing, validation, deployment, and rollback processes.
• Enable scalable serving infrastructure for ML and GenAI applications.
• Support batch, real-time, and streaming inference architectures.
• Build and manage enterprise AI platforms on cloud and hybrid environments.
• Implement containerized AI workloads using Docker and Kubernetes.
• Establish scalable GPU-enabled environments for model training and inference.
• Monitor model accuracy, drift, bias, performance, latency, and usage.
• Establish alerting and automated remediation mechanisms.
• Develop AI observability dashboards and operational metrics.
• Lead root cause analysis for AI platform and model performance issues.

4. Data & Feature Management

• Establish data lineage and model traceability mechanisms.
• Define data quality controls and validation standards.
• Govern training, validation, and inference datasets.
• Ensure reproducibility and auditability across AI pipelines.

5. Generative AI Operations

• Operationalize GenAI and LLM-based solutions.
• Build deployment frameworks for RAG, Agentic AI, and LLM applications.

5anage prompt versioning, evaluation frameworks, and model governance.

• Define monitoring controls for LLM performance, hallucination rates, and response quality.
• Support model orchestration frameworks including LangChain, LangGraph, CrewAI, and Vertex AI Agents.

6. Stakeholder Management

• Collaborate with business leaders, AI teams, operations teams, and executive stakeholders.
• Translate business requirements into scalable AI operational solutions.
• Present AI operational status and platform roadmap updates to leadership.
• Manage vendor and cloud provider engagements.

Required Technical Skills

Skill Area

Expected Competency

MLOps Platforms

DevOps & Automation

Jenkins, GitHub Actions, GitLab CI/CD,

Containerization

Infrastructure as Code

Terraform, CloudFormation, Ansible

Programming

Python, SQL

BigQuery, Snowflake, Spark, Airflow

GenAI Platforms