ML Engineer

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ML Engineer

R5561

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 Customer and Business Insights group delivers Data as a Product (DaaP), transforming raw customer and business data into actionable insights for personalization, experimentation, and strategic decision-making. It manages end-to-end data processing, experimentation platforms, and insight generation, creating trusted, reusable data products for analytics and AI-driven experiences. The mission is to enable real-time intelligence on customer profiles, preferences, and behaviors, driving scalable insights that optimize performance and global user engagement  

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:

AI/ML Pipeline & Model Lifecycle

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

  • Algorithm development, data pipeline construction, model optimization, and ensuring scalability

  • Use deep learning frameworks (TensorFlow/PyTorch)to build, maintain and trouble shoot AI/ML and GenAI pipelines

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

Build AI-Powered Platform Frameworks and Tooling

  • Build AI-powered Python apps using internal platform tooling deployed to GKE K8s clusters with GitOps-based deployment workflows with GitHub Actions, ArgoCD and Codefresh. 

  • Collaborate on Improving 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.
     

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.
     

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

  • Strong experience with Deep learning frameworks (TensorFlow/PyTorch)

  • Good to have 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 LLMs (RAG pipelines, vector stores, prompt/version management, agent frameworks).

  • Good to have 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.

  • Strong proficiency in data science and analytics. 

  • 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.   
    #LI-hybrid

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