Senior Platform Engineer

Priceline.com LLC; Mumbai; Hybrid; INR 3,000,000 - 6,000,000; Full time

Benefits offered by this job

Health & wellness coverage

Generous time off and Priceline Pause

Work/life support and remote work

Travel perks and education benefits

Job summary

Priceline.com LLC in Mumbai is seeking a Senior Platform Engineer to shape MLOps and DataOps platforms, focusing on Terraform-based infrastructure, CI/CD and scalable deployments. You will drive automation, governance, and reliable delivery of data and ML workloads across development, QA, and production environments.

You will join a team enabling data, ML, and GenAI workloads with Vertex AI, Airflow/Composer, BigQuery, and GCP services, emphasizing observability, security, and cost controls.

Qualifications

• 7+ years in DevOps/SRE/cloud platform engineering.
• Strong hands-on with GCP and related data/ML services.
• Experience building CI/CD pipelines with GitHub Actions, Argo CD or Codefresh.
• Proficiency in Python and Shell scripting.
• Excellent collaboration and communication skills.

Responsibilities

• Architect, govern, and scale Terraform-based infrastructure for data and ML platforms.
• Define and improve CI/CD and GitOps pipelines for Airflow and ML workloads.
• Operate ML pipelines, model endpoints, and GenAI workloads on Vertex AI and Kubernetes.
• Ensure security, cost visibility, observability, and governance across platforms.

Skills

GCP

Terraform

Docker

Kubernetes

Airflow

Vertex AI

CI/CD

GitHub Actions

Python

Shell scripting

SRE

Tools

Terraform

Docker

Kubernetes

GKE

Airflow/Composer

Job description

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

Senior 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: 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. 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.

5. Cost, Governance, Leadership & Platform Enablement: 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:

• 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.
• 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.
• Strong practical experience with Terraform for infrastructure provisioning, configuration management, reusable modules, and controlled production changes.
• Hands‑on experience with Docker and Kubernetes. Experience with GKE is preferred for GCP environments.
• Experience building or operating CI/CD pipelines and Git‑based deployment workflows using GitHub Actions, Argo CD, Codefresh, Jenkins, or equivalent tools.
• Proficiency in Python and Shell scripting for platform automation, operational tooling, pipeline hooks, and internal utilities.
• Experience supporting Apache Airflow/Composer, Dataproc, Dataflow, Spark, BigQuery, or comparable data‑processing and workflow‑orchestration technologies.
• Experience supporting ML pipelines, model deployments, inference endpoints, model‑serving infrastructure, or ML platform operations.
• Experience with monitoring, logging, alerting, incident response, on‑call support, troubleshooting, and root‑cause analysis for production systems.
• Working knowledge of IAM, RBAC, service accounts, secrets, vulnerability management, networking fundamentals, and secure cloud operations.
• 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.

Who We Are

WE ARE PRICELINE. Our success as one of the biggest players in online travel is all thanks to our incredible, dedicated team of talented employees. Priceliners are focused on being the best travel deal makers in the world, motivated by our passion to help everyone experience the moments that matter most in their lives. Whether it’s a dream vacation, your cousin’s graduation, or your best friend’s wedding - we make travel affordable and accessible to our customers.

Our culture is unique and inspiring (that’s what our employees tell us). We’re a grown‑up, startup. We deliver the excitement of a new venture, without the struggles and chaos that can come with a business that hasn’t stabilized. We’re on the cutting edge of innovative technologies. We keep the customer at the center of all that we do. Our ability to meet their needs relies on the strength of a workforce as diverse as the customers we serve. We bring together employees from all walks of life and we are proud to provide the kind of inclusive environment that stimulates innovation, creativity and collaboration.

Priceline is part of the Booking Holdings, Inc. (Nasdaq: BKNG) family of companies, a highly profitable global online travel company with a market capitalization of over $80 billion. Our sister companies include Booking.com, BookingGo, Agoda, Kayak and OpenTable.

Flexible work at Priceline

Priceline is following a hybrid working model, which includes two days onsite as determined by you and your manager (ideally selecting among Tuesday, Wednesday, or Thursday). On the remaining days, you can choose to be remote or in the office.

Perks & Benefits at Priceline

• Health & wellness coverage including medical, dental, vision, and mental health resources
• Generous time off including PTO, holidays, a company‑wide Priceline Pause reset week, and paid volunteer days
• Work/life support including the ability to work up to 4 weeks per year from anywhere, parental leave, dependent care and family support resources, Summer Fridays, and office perks like stocked kitchens and catered meals (varies by location)
• Financial security programs such as retirement plans with company contributions, life and disability coverage, and tax‑advantaged accounts
• Signature travel perks including employee‑only discounts on hotels and flights, VIP deals, and Big Deal Bucks credits
• Additional perks & discounts like travel and partner discounts, tuition support, legal support, and pet benefits

A people‑first culture with Employee Resource Groups (ERGs), social events, recognition programs, and service awards that help you connect, grow, and celebrate together. Specific benefits and programs vary by location.

Inclusion is a Big Deal!

We believe our team should reflect the broad range of customers and communities we serve. We are committed to cultivating a culture where all employees have the freedom to bring their individual perspectives, life experiences, and passion to work. Priceline is a proud equal opportunity employer. We embrace and celebrate the unique lenses through which our employees see the world. We’d love for you to join us and help shape what makes our team extraordinary.