Key Responsibilities

- Pipeline Orchestration: Design, develop, and maintain complex ML workflows using Apache Airflow (Cloud Composer) to automate data ingestion, preprocessing, and model training.

- Lifecycle Management: Administer and scale MLflow for experiment tracking, model packaging, and maintaining a centralized Model Registry across the organization.

- Cloud &
- Hybrid Ops: Create and optimize training environments for custom ML/LLM models.

- Model Serving &

- Scaling: Architect high-performance inference endpoints and serve models via FastAPI/Flask with API Gateway.

- Infrastructure Management: Manage auto-scaling CUDA clusters on Google Kubernetes Engine (GKE).

- CI/CD: Manage end-to-end delivery with Continuous Integration &

- Continuous Delivery (CI/CD).

- Observability &

- Monitoring: Build dashboards to track model health, latency, and data drift.

Requirements &

- Skills

- Workflow Management: Experience in managing Apache Airflow and Composer to support the Data Engineering components of grounded AI solutions.

- MLflow: Deep knowledge of MLflow Tracking, Projects, and Registry.

Experience migrating MLflow backends between cloud providers.

- Workflow Tools:



Familiarity with Vertex AI Pipelines and Azure DevOps for automation.

- GCP AI Services: Practical experience with Vertex AI (Workbench, Model Garden, Feature Store) and BigQuery ML.

- Containerization: Expert-level Docker and Kubernetes (GKE/AKS) skills. Must understand K8s operators and resource management for ML workloads.

- Infrastructure as Code (IaC): Proficiency in Terraform to manage reproducible cloud environments.

- Programming: Advanced Python skills with a focus on software engineering best practices (unit testing, modular design).

- Data Engineering: Experience with Change Data Capture (CDC), Spark/PySpark, and optimizing data flow from BigQuery to training nodes.

- Access Control: Knowledge of IAM roles, VPC Service Controls, and securing ML endpoints.

- Experience with LLMOps (managing large-scale foundation models, prompt versioning, and vector database scaling).

- Proven track record of building enterprise-scale ML infrastructure from scratch in Azure/GCP.

- Related MLOps Engineering certifications are beneficial.

📌 MLOps Engineer (Bangalore Rural)
🏢 Talent Drive Consulting
📍 Bangalore Rural