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.

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 .

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.

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.

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.

Monitoring, Observability, Security & SRE

Define and operate monitoring, logging, alerting, and reliability practices for data pipelines, ML pipelines, model endpoints, and platform services.

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.

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.

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.

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.

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.

We're a grown-up, startup. We're on the cutting edge of innovative technologies. Nasdaq: BKNG) family of companies, a highly profitable global online travel company with a market capitalization of over $80 billion. Adaptable 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.

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

Inclusion is a Big Deal!

To be the best travel dealmakers in the world, 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.

We'd love for you to join us and help shape what makes our team extraordinary.

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📌 Senior DevOps Engineer / Cloud Platform Engineer (Mumbai)
🏢 Priceline
📍 Mumbai