Platform Engineer

This role is eligible for our hybrid work model: Two days in-office. Based in our Mumbai office.
Platform Engineer (MLOps & DataOps)
The MLOps & Data DevOps team enables Priceline’s data engineering, machine learning, and GenAI teams through secure, scalable, reliable, and governed platforms.
We build and operate the infrastructure, automation, deployment pipelines, observability, and self-service capabilities that allow engineering teams to deliver data and ML workloads safely to production.
If you enjoy working at the intersection of cloud infrastructure, DevOps, SRE, data platforms, and machine learning operations, this role offers an opportunity to build platform capabilities used across Priceline.
Why this job’s a big deal
As Priceline expands its use of data, real-time analytics, machine learning, and GenAI, reliable platform engineering is critical to delivering business value safely and efficiently.
You will help operate the platform foundation behind data pipelines, ML pipelines, model endpoints, and LLM workloads. Your work will improve deployment speed, platform reliability, cost visibility, security, and the self-service experience for data and ML engineering teams.
This role is focused on platform engineering and operations. Data and ML teams own business logic, model development, testing, and domain-level data quality; the MLOps & DataOps team owns the infrastructure, deployment paths, automation, governance, and reliability practices that enable those workloads.
In this role, you will:
1. Cloud Platform Infrastructure & Automation
Build, maintain, and improve Terraform modules and configurations for GCP data and ML platform services.
Provision, operate, and automate cloud data and ML platform services such as Cloud Composer/Airflow, Dataproc, Dataflow, BigQuery, Cloud Storage, and Vertex AI.
Support platform upgrades, configuration changes, capacity planning, and operational improvements across development, QA, and production environments.
Develop reusable automation and internal tools using Python and Shell scripting.
Apply infrastructure-as-code, change-management, security, and auditability standards to platform changes.
2. CI/CD, GitOps & Self-Service Enablement
Build and operate governed CI/CD pipelines for Airflow DAGs, Dataproc jobs, BigQuery changes, ML pipelines, and model-serving infrastructure.
Implement reliable deployment, promotion, validation, rollback, and release processes using Git-based workflows.
Work with GitHub Actions, Argo CD, Codefresh, or equivalent CI/CD and GitOps tooling.
Create self-service patterns that allow data and ML teams to provision and deploy workloads safely without bypassing governance controls.
Improve developer experience, deployment reliability, and time to production through standardization and automation.
3. MLOps & LLMOps Platform Operations
Support production ML workflows covering model training, pipeline execution, model deployment, endpoint operations, batch inference, and retraining.
Operate and improve ML platforms based on Vertex AI, Kubernetes/GKE, Docker, Kubeflow, or equivalent technologies.
Support model gateway and LLM platform capabilities such as LiteLLM, including model onboarding, access management, rate limiting, usage visibility, cost controls, and reliability improvements.
Help establish repeatable practices for model versioning, artifact management, model registries, feature stores, and data/model lineage.
Contribute to GenAI, RAG, model evaluation, guardrails, and AI observability capabilities.
4. Monitoring, Observability, Security & SRE
Define and operate monitoring, logging, alerting, and reliability practices for data pipelines, ML pipelines, model endpoints, and platform services.
Establish meaningful SLIs/SLOs, escalation paths, runbooks, and error-management practices.
Use tools such as PagerDuty, Splunk, New Relic, Arize, or equivalent observability platforms.
Troubleshoot pipeline failures, deployment issues, infrastructure problems, endpoint failures, and performance degradation.
Participate in on-call support, contribute to root-cause analysis, and drive corrective and preventative actions
Integrate IAM, RBAC, service-account controls, vulnerability scanning, secrets management, and compliance guardrails into platform workflows.
5. Cost, Governance & Platform Enablement
Improve cloud cost visibility, resource utilization, capacity management, and FinOps practices for data and ML workloads.
Evaluate, adopt, and operationalize platform capabilities that improve reliability, security, developer productivity, and operational efficiency.
Partner with Cloud Infrastructure, SRE, InfoSec, Data Engineering, Data Science, and ML teams to resolve platform and operational issues.
Document platform standards, operational procedures, troubleshooting guidance, and reusable deployment patterns.
Who you are:
Degree & Experience: B.Tech or B.E in Computer Science, Information technology, or equivalent practical experience, with 4–6 years of experience in DevOps, SRE, cloud platform engineering, MLOps, DataOps, or a closely related discipline.
Cloud Infrastructure: Strong hands-on experience with Google Cloud Platform or equivalent public cloud technologies. Experience with GCP services such as Cloud Composer, Dataproc, Dataflow, BigQuery, Google Cloud Storage, or Vertex AI is highly relevant.
Infrastructure as Code: Strong practical experience with Terraform for infrastructure provisioning, configuration management, reusable modules, and controlled production changes.
Containers & Kubernetes: Hands-on experience with Docker and Kubernetes. Experience with GKE is preferred for GCP environments.
CI/CD & GitOps: Experience building or operating CI/CD pipelines and Git-based deployment workflows using GitHub Actions, Argo CD, Codefresh, Jenkins, or equivalent tools.
Automation & Scripting: Proficiency in Python and Shell scripting for platform automation, operational tooling, pipeline hooks, and internal utilities.
Data & Workflow Platforms: Experience supporting Apache Airflow/Composer, Dataproc, Dataflow, Spark, BigQuery, or comparable data-processing and workflow-orchestration technologies.
MLOps: Experience supporting ML pipelines, model deployments, inference endpoints, model-serving infrastructure, or ML platform operations.
Reliability & Operations: Experience with monitoring, logging, alerting, incident response, on-call support, troubleshooting, and root-cause analysis for production systems.