Senior Platform Engineer

Overview

Senior Platform Engineer on Priceline's MLOps & DataOps team, building and operating the infrastructure, automation, deployment pipelines, observability and self-service capabilities that let data, ML and GenAI teams ship workloads to production safely.

What you'll do

• 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 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.
• Design scalable self-service frameworks that let data and ML teams provision and deploy workloads without bypassing governance controls.
• Architect and optimize production ML workflows covering 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 standards for model versioning, artifact management, model registries, feature stores and data/model lineage.
• Drive GenAI, RAG, model evaluation, guardrails and AI observability capabilities.
• Define and operate monitoring, logging, alerting and reliability practices, including meaningful SLIs/SLOs, escalation paths, runbooks and error-management practices.
• Lead tier-3 incident response for complex infrastructure failures and drive architectural changes to prevent recurrence.
• Participate in on-call support, lead blameless root-cause analysis 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 MLOps and DataOps.
• Own and optimize cloud architecture for cost efficiency, 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.

What you'll need

• B.Tech or B.E in Computer Science, Information Technology, or equivalent practical experience.
• 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.
• Strong practical experience with Terraform for infrastructure provisioning, configuration management, reusable modules and controlled production changes.
• Hands-on experience with Docker and Kubernetes.
• 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 have

• 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.

Details

• Location: Mumbai office.
• Hybrid work model: two days in-office, selected with your manager (ideally Tuesday, Wednesday or Thursday); remaining days remote or in office.