Senior MLOps Engineer / Senior Mlops Manager

Nityo Infotech Limited; Bengaluru; Hybrid; INR 3.000.000 - 5.500.000; Vollzeit

Zusammenfassung

Nityo Infotech Limited is seeking a Senior MLOps Engineer in Bengaluru to design and implement robust MLOps pipelines and governance. The role focuses on automated CI/CD promotion, MLflow tracking with Unity Catalog, and reusable Databricks workflows for training, validation, and deployment, while enforcing security and cost controls.

You will automate environments with Terraform, integrate Key Vault, and build production-grade serving, monitoring, and rollback patterns, ensuring sandbox

Qualifikationen

• Proven experience building end-to-end MLOps pipelines.
• Hands-on with cloud-based controls, RBAC, and IAM.
• Strong automation using IaC and CI/CD tooling.

Aufgaben

• Build CI/CD pipelines for controlled promotion from dev/test to prod.
• Implement MLflow tracking and Unity Catalog-based model registry.
• Create reusable Databricks Jobs and Workflows for training, validation, registration, deployment, and retraining.
• Automate environment provisioning and controls using Terraform.
• Implement RBAC, service principals, and Key Vault integration.
• Build model serving, monitoring, and rollback patterns.
• Add usage logging, cost telemetry, and market attribution mechanisms.
• Create guardrails for sandbox/development environments without prod rights.
• Operationalize RAG and GenAI workloads with observability and security.

Kenntnisse

CI/CD

Azure DevOps

GitHub Actions

MLflow

Databricks

Terraform

RBAC & IAM

Cost telemetry

Tools

Unity Catalog

Model registry

Key Vault

Databricks Jobs

Jobbeschreibung

Role: Senior MLOps Engineer

Role Mandate: Build the technical controls, automation, and deployment pipelines that make the centralized MLOps operating model work in practice.

Key Responsibilities

• Build CI/CD pipelines for controlled promotion from development/test to production using Azure DevOps or GitHub Actions.
• Implement MLflow tracking and Unity Catalog-based model registry and promotion patterns.
• Create reusable Databricks Jobs and Workflows for training, validation, registration, deployment, and retraining.
• Automate environment provisioning and controls using Terraform and Infrastructure as Code.
• Implement RBAC, service principals, managed identities, Key Vault integration, and production access restrictions.
• Build model serving, batch inference, monitoring, alerting, and rollback patterns.
• Implement usage logging, tags, compute policies, and cost telemetry for market and use-case attribution.
• Build guardrails so market users can work independently in sandbox and development environments without direct production deployment rights.
• Operationalize RAG and GenAI workloads with evaluation, observability, security, and cost controls.