Experience

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

Key Responsibilities

1.Machine Learning Model Deployment

- 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.

2. AI Platform Engineering

- 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.

3. Model Monitoring & Observability

- Implement end-to-end model monitoring frameworks.
- 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

- Implement enterprise Feature Store frameworks.
- 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

Vertex AI, MLflow, Kubeflow

Cloud Platforms

Google Cloud Platform, Microsoft Azure, AWS

DevOps & Automation

Jenkins, GitHub Actions, GitLab CI/CD,

Containerization

Docker, Kubernetes, OpenShift

Infrastructure as Code

Terraform, CloudFormation, Ansible

Programming

Python, SQL

Data Engineering

BigQuery, Snowflake, Spark, Airflow

GenAI Platforms

Vertex AI, OpenAI APIs, LangChain, LangGraph

📌 Machine Learning Lead (Sydney)
🏢 BULLIT MANAGEMENT SERVICES
📍 Sydney