Senior AI Engineer

• Design, build, and deploy generative AI applications using Google Gemini (Pro, Ultra, Flash), PaLM 2, and other Google-hosted foundation models via Vertex AI. 

• Implement Retrieval-Augmented Generation (RAG) architectures using Vertex AI Search, Vector Search, and document embedding pipelines for enterprise knowledge retrieval. 

• Develop multi-modal AI capabilities leveraging Gemini's vision, text, and code understanding for hospitality use cases such as guest experience, analytics, and operations. 

• Build and maintain agentic AI workflows and orchestration using LangChain, LlamaIndex, or Google Agent Builder — integrating tools, APIs, and enterprise data sources. 

• Optimize prompt engineering strategies, system instructions, and grounding mechanisms for production-grade LLM deployments. 

1. Data Science & ML Engineering

• Develop end-to-end ML pipelines from data ingestion and feature engineering through model training, evaluation, and production deployment on Vertex AI Pipelines / Kubeflow. 

• Apply advanced data science techniques — statistical modelling, time-series forecasting, recommendation systems, and anomaly detection — for hospitality and gaming analytics. 

• Build scalable data transformation and feature engineering workflows using BigQuery, Dataflow, and Pub/Sub. 

• Implement model monitoring, drift detection, and automated retraining strategies to ensure sustained model performance in production. 

• Leverage TensorFlow, JAX, or PyTorch for custom model development where pre-trained solutions are insufficient. 

1. GCP Platform & Cloud Architecture

• Architect and manage cloud-native AI infrastructure on GCP — including Vertex AI, BigQuery ML, Cloud Run, GKE, Cloud Functions, and Cloud Storage. 

• Design secure, scalable, and cost-optimized GCP environments aligned with enterprise compliance requirements and CLIENT's data governance standards. 

• Implement CI/CD pipelines for ML model serving using Cloud Build, Artifact Registry, and Vertex AI Model Registry. 

• Set up monitoring, observability, and alerting for AI/ML workloads using Cloud Monitoring, Cloud Logging, and custom dashboards in Looker. 

• Build agents with Google ADK, deploy with Cloud Run / Agent Engine with Vertex. 

• Design and implement conversational AI agents using Dialogflow CX and Agent Builder for guest-facing and internal automation use cases. 

1. Collaboration, Governance & Mentorship

• Partner with CLIENT's business and technology stakeholders to define AI use cases, prioritize the roadmap, and translate requirements into technical deliverables. 

• Champion responsible AI practices — model fairness, explainability, content safety, and data privacy — across all AI solution designs. 

• Produce and maintain technical documentation including architecture decision records (ADRs), API specs, model cards, and runbooks. 

• Mentor junior engineers and lead knowledge-sharing sessions; contribute to AI community of practice within the delivery organization. 

AS A SENIOR AI ENINEER, you will:

• Lead end-to-end design and delivery of AI modules — from architecture to production deployment — for complex, multi-component features. 

• Define LLM integration patterns, RAG strategies, and data pipeline architectures; own technical quality and performance of these systems. 

• Act as the primary technical interface with cross-functional stakeholders at CLIENT; participate in requirements workshops and solution demos. 

• Drive design reviews, establish engineering standards, and actively mentor L2 engineers on the team. 

• Identify and address technical debt, reliability risks, and scalability bottlenecks proactively. 

• Build agents with Google ADK, deploy with Cloud Run / Agent Engine with Vertex. 

QUALIFICATIONS & EDUCATION 

• Bachelor's or Master's degre