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 stakehol ders.
- 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

Location

Sydney

Job Function

TECHNOLOGY

Role

Operations Head

Job Id

432500

Desired Skills

Machine Learning

📌 MLOps (Sydney)
🏢 Tata Consultancy Services (TCS)
📍 Sydney