Senior Platform Engineer

Priceline; Mumbai; Hybrid; INR 3,800,000 - 5,600,000; Full time

Benefits offered by this job

Hybrid work model (2 in-office days)

Job summary

Priceline Careers in Mumbai is seeking a senior platform engineer to own MLOps, DataOps, and cloud platform operations. You will architect, deploy, and govern the data/ML platform, integrating Terraform, GKE, Vertex AI, Airflow, and DevOps practices.

The role focuses on platform reliability, security, and scalable automation across data pipelines, ML pipelines, and model endpoints, working with cross-functional teams in a hybrid setup. 7+ years of relevant experience is expected.

Qualifications

• B.Tech or B.E in Computer Science or equivalent practical experience.
• 7+ years in DevOps, SRE, cloud platform engineering, MLOps, DataOps or related.
• Strong hands-on experience with Google Cloud Platform and GCP services such as Composer, Dataproc, Dataflow, BigQuery, Cloud Storage, Vertex AI.
• Terraform, Docker, Kubernetes experience with IaC and production changes.
• Python and Shell scripting for automation and tooling.
• Experience with Airflow/Composer, Dataproc, Dataflow, BigQuery and related data platforms.

Responsibilities

• Architect, govern, and scale Terraform module registries and IaC strategies for GCP data/ML services.
• Provision, operate, and automate Cloud data/ML platform services (Composer, Dataproc, Dataflow, BigQuery, Vertex AI).
• Drive platform upgrades, capacity planning, and operational improvements across environments.
• Develop reusable automation and internal tools using Python and Shell scripting.
• Apply infrastructure-as-code, security, and auditability standards to platform changes.
• Define CI/CD enterprise architecture and governed pipelines for Airflow, Dataproc, BigQuery, ML pipelines, and model-serving infra.
• Implement reliable deployment, promotion, validation, rollback, and release processes with Git-based workflows.
• Set strategy for GitHub Actions, Argo CD, Codefresh, Jenkins, or equivalent tools; improve developer experience.
• Design scalable self-service frameworks for data/ML workloads with governance controls.
• Improve deployment reliability and time to production through standardization and automation.
• Architect and optimize ML workflows for training, deployment, endpoints, and retraining.
• Operate ML platforms based on Vertex AI, Kubernetes/GKE, Docker, Kubeflow, or equivalents.
• Support model gateway and LLM platform capabilities including access, rate limiting, and cost controls.
• Define and enforce standards for model versioning, artifact management, and data/model lineage.
• Drive GenAI, RAG, guardrails, and observability capabilities.
• Define monitoring, logging, SLIs/SLOs, incident response, RCAs, and runbooks.

Skills

DevOps

SRE

Cloud platform engineering

MLOps

DataOps

Python scripting

Shell scripting

Terraform

Docker

Kubernetes

CI/CD

GitOps

Airflow/Composer

Data processing

BigQuery

Vertex AI

Education

B.Tech or B.E in Computer Science

Tools

GKE

Google Cloud Platform (GCP)

GitHub Actions

Argo CD

Codefresh

Jenkins

Airflow/Composer

Dataproc

Dataflow

Vertex AI

Job description

This role is eligible for our hybrid work model: Two days in-office. Based in our Mumbai office.

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:

• Architect, govern, and scale enterprise-grade Terraform module registries and infrastructure-as-code strategies 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.
• Drive platform upgrades, lead capacity planning, and design 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

• Define the CI/CD enterprise architecture and build governed 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.
• Set the strategy for GitHub Actions, Argo CD, Codefresh, or equivalent CI/CD and GitOps tooling to improve developer experience and time to production.
• Design and engineer scalable self-service frameworks 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

• Architect and optimize 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.
• Define and enforce repeatable enterprise standards for model versioning, artifact management, model registries, feature stores, and data/model lineage.
• Drive 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.
• Lead tier-3 incident response for complex infrastructure failures and drive systemic architectural changes to prevent recurrence.
• Lead tier-3 incident response for complex infrastructure failures and drive systemic architectural changes to prevent recurrence.
• Participate in on-call support, lead blameless root-cause analysis (RCAs), and enforce corrective and preventative actions.
• Architect IAM, RBAC, service-account controls, vulnerability scanning, secrets management, and compliance guardrails into platform workflows.
• Mentor mid-level and junior engineers, lead complex cross-team technical initiatives, and establish engineering standards for the broader MLOps and DataOps domains.
• Own and optimize cloud architecture for cost efficiency, actively managing capacity and FinOps practices for large-scale 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.

Who you are:

• Degree & Experience: B.Tech or B.E in Computer Science, Information technology, or equivalent practical experience, with 7+ 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.
• Security & Governance: Working knowledge of IAM, RBAC, service accounts, secrets, vulnerability management, networking fundamentals, and secure cloud operations.
• Operational Mindset: Strong ownership, structured problem-solving, documentation, communication, and collaboration skills.

Nice-to-Haves:

• Experience operating LLM gateways or GenAI platforms such as LiteLLM, Tyk, or equivalent model-access layers.
• Experience with ML/LLM observability and evaluation platforms such as Arize, LangSmith, or equivalent tools.
• Experience with GKE platform operations, GPU workloads, vector databases, RAG infrastructure, or model-serving systems.
• Experience implementing data, model, artifact, or pipeline lineage and reproducibility practices.
• Experience with cloud cost optimization, capacity management, and FinOps for data and ML workloads.
• Experience applying SRE, security, networking, or compliance practices to cloud data and ML platforms.