Senior AI Engineer

Roboyo is a category shaper in Agentic Automation. We help leading brands embed autonomous, AI‑powered agents into their workflows, processes, products and services so they can scale faster and operate smarter.

Built on a strong automation heritage, we focus on seamless integration of AI into enterprise level organization, not just proving concepts, but owning outcomes and driving value in every industry we are present. At Roboyo, you’ll join a global team of builders, consultants and engineers that are top practitioners of taking solutions to the next level for clients in pursuit of excellence.

 

ABOUT THE ROLE 

We are looking for Senior AI Engineers to join this engagement and help build, scale, and govern AI-powered products on the Google Cloud Platform ecosystem. You will work at the intersection of Data Science, Generative AI, and Cloud Engineering — leveraging the full Google AI tech stack to deliver measurable business outcomes for one of the world's most recognized enterprise brands. 

CORE TECHNOLOGY SKILLS 

Google Cloud Platform	Vertex AI	Gemini Pro / Ultra	BigQuery
Dataflow / Pub/Sub	Cloud Run / GKE	LangChain / LlamaIndex	RAG Pipelines
Python	TensorFlow / JAX	Looker / Data Studio	MLflow / Kubeflow
Google Workspace APIs	Vector Search (Vertex)	Data Science	GenAI APIs

 

KEY RESPONSIBILITIES 

1. Generative AI & LLM Development

• 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 degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related technical field. 

• 6–10 years of experience with 5+ years in AI/ML engineering; prior experience leading technical workstreams in enterprise settings. 

• Strong proficiency in Python; working knowledge of SQL; familiarity with infrastructure-as-code tools (Terraform, Cloud Deployment Manager) preferred. 

• Google Cloud Professional certifications (Cloud Architect, ML Engineer, Data Engineer) are a strong differentiator at all levels. 

• Excellent written and verbal communication skills; ability to present technical concepts to non-technical executive audiences. 

GOOD-TO-HAVE SKILLS 

• Experience with hospitality, gaming, or retail domains — understanding CLIENT's operational context is a significant advantage. 

• Familiarity with additional Vector DB platforms such as Pinecone, Weaviate, pgvector, or Chroma alongside Vertex AI Vector Search. 

• Knowledge of RPA tools or workflow automation platforms (UiPath, Power Automate) that complement AI pipelines. 

• Exposure to responsible AI toolkits, model interpretability frameworks (SHAP, LIME), and AI governance practices. 

• Experience with real-time inference architectures, streaming ML, and event-driven AI using Pub/Sub and Eventarc. 

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

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