ML Engineer

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ML Engineer

R5381

Location

Mumbai

Career Track

Technology

ML Engineer

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

The Global Platform Engineering platform team within the Data & AI/ML product group powers Priceline’s most strategic initiatives by providing secure, scalable, and governed access to data, machine learning, and GenAI. We build the core platforms that enable self-service development and deliver reliable, enterprise-ready solutions. If you enjoy working at the intersection of platform engineering and AI/ML at scale, this team offers a high-impact environment.

Why this job’s a big deal:

As Priceline scales personalization, AI-driven optimization, and intelligent automation, a modern platform for data and AI/ML is mission-critical. You’ll help build the foundational systems that enable self-service AI-powered software engineering, data engineering, MLOps/LLMOps, and GenAI workloads, while democratizing access to trusted data and models. Your work will directly accelerate innovation across every product and business team at Priceline. 

In this role you will get to:
 

Build AI-Powered Platform Frameworks and Tooling

  • Build AI-powered Python apps and internal platform tooling deployed to GKE K8s clusters with GitOps-based deployment workflows with GitHub Actions, ArgoCD and Codefresh. Leverage Istio gateways and service mesh patterns for traffic management.

  • Improve developer productivity around GenAI usage by providing standardized templates, self-serve workflows, and reusable tooling around AI/ML workloads. This includes tooling built using popular GenAI frameworks such as LangChain, LangGraph, etc. as well as retrieval-augmented generation (RAG) and agent-based application patterns.

  • Collaborate on building guardrails and observability frameworks for LLMs—covering evaluation (evals), prompt management, safety, and cost optimization. Develop scalable evaluation pipelines for LLM-based agents and integrate monitoring tools (e.g., Arize, LangSmith, LiteLLM). Contribute to frameworks enabling secure, policy-aligned, and explainable AI deployments.
     

AI/ML Pipeline & Model Lifecycle

  • Build, maintain, and troubleshoot AI/ML and GenAI pipelines, batch jobs, and custom workflows leveraging Composer (Airflow), Vertex AI, Dataproc, Dataflow etc. Support workflow orchestration for AI/ML workloads, spanning data preparation, evaluation, and deployment stages.

  • Collaborate with centralized infrastructure platform teams and security teams to integrate enterprise security tooling such as NexusIQ, StackRox, and Wiz into AI/ML workflows.

  • Implement and standardize LLMOps patterns using Gemini Enterprise, LiteLLM, or similar model gateways to enable governed model access.
     

Monitoring, Observability & Model Quality

  • Use Arize (or similar tools) for model drift/quality monitoring, embeddings monitoring, and LLM evaluation patterns.

  • Implement logging, alerting, and SLOs for ML workloads and pipelines with Splunk, New Relic, Pagerduty etc.

  • Assist with incident response, root-cause analysis, and long-term platform improvements.

Who you are:

  • Bachelor’s Degree in Computer Science or relevant experience.

  • 4–6 years of experience in Cloud-native software engineering.

  • Strong experience with GCP (Vertex AI, GKE, Dataflow, Dataproc, Composer, BigQuery, etc.) or other major cloud providers (Azure/AWS).

  • Hands-on expertise with Kubernetes, Vertex AI, Docker and image-based deployment workflows.

  • High proficiency with Python or similar object oriented programming language, especially for developing AI-powered apps.

  • Experience with LLMOps toolchains (RAG pipelines, vector stores, prompt/version management, agent frameworks).

  • Experience deploying apps via GitOps using ArgoCD or similar.

  • Proven ability to support AI/ML models in production: monitoring, pipelines, debugging, retraining loops.

  • Eagerness to learn new techniques, technologies, solve problems and contribute in a team environment.

  • Illustrated history of living the values necessary to Priceline:  Customer, Innovation, Team, Accountability and Trust. 

  • The Right Results, the Right Way is not just a motto at Priceline; it’s a way of life. Unquestionable integrity and ethics is essential.  
     

Nice-to-Haves:-

  • Familiarity with enterprise MLOps tooling and best practices.

  • Good understanding of infosec and RBAC best practices, and security posture management.

  • Exposure to SRE best practices and error budgets for AI/ML systems.
    #LI-hybrid

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