Principal GenAI & Agentic AI Engineer
Sabre India · Bengaluru, Karnataka, India ·
- Seniority
- Principal
- Category
- AI engineering
- Experience
- 15+ years
- Company size
- 1000+
Sabre India · Bengaluru, Karnataka, India ·
aresmgmt · New York, NY
pureinsurance · White Plains
Ingram Micro · Irvine, CA, United States of America
LTS - Video Solutions for Security Professionals · United States - Remote
Leads architecture and delivery of enterprise-scale GenAI and agentic AI systems on GCP, defining standards, pipelines, and guardrails to power AI-native travel retailing platforms.
Powering the agentic revolution in travel. Sabre is an AI-native technology leader, backed by one of the world’s largest travel data clouds. Built on an open, modular, cloud-native architecture, Sabre serves as the backbone for both established leaders and bold, new disruptors, guiding them to the next age of travel retailing through intelligent, connected, and personalized experiences. With AI at its core and operating at unparalleled scale, Sabre transforms insights into innovation, empowering airlines, hoteliers, agencies and other partners to retail, distribute and fulfill travel worldwide.
The Principal GenAI & Agentic AI Engineer is the technical leader responsible for designing, building, and scaling AI systems that combine LLM-powered GenAI and ADK-based agentic workflows on Google Cloud Platform. This role also requires leading and developing data pipelines for necessary data layer for AI/ML. This role sets architecture standards, leads multi-team delivery, and governs safety, reliability, and cost at enterprise scale—accelerating product teams to achieve monetization of AI based products through reusable patterns, platforms, and guardrails.
Key Responsibilities
Strategy & Architecture
Define reference architectures for GenAI apps, RAG systems, and agent ecosystems (single/multi-agent) on GCP using ADK.
Leverage capabilities of Gemini Enterprise Agent Platform in the Agentic AI Product development.
Establish domain and platform standards: model selection, RAG/generation patterns, memory architectures, security baselines, observability, and LLMOps.
Lead portfolio-wide technical decisions (build/buy, vendor selection, SLAs, quotas) with a focus on reliability, safety, and cost control.
Define the data pipeline development for lakehouse, delta lake or feature engineering.
Solution Design & Delivery
Architect and lead implementation of production-grade GenAI solutions (Vertex AI models, Grounding, Pipelines, Evaluation) and agentic services (planning, tools, memory, HIL).
Design multi-tenant and hub-and-spoke patterns with Okta/IAP/Apigee for secure API exposure and tenant isolation.
Drive end-to-end delivery across teams: data ingestion (Dataflow/Composer), indexing (BigQuery vectors/Vertex Vector Search), services (Cloud Run/Workflows), events (Pub/Sub).
Data Pipeline both near real time and batch.
Platformization & Reuse
Build and maintain prompt libraries, tool catalogs, agent templates, and evaluation harnesses for organization-wide reuse.
Standardize LLMOps: CI/CD for prompts/models/agents, model registry, traceability, rollback, canaries, cost/performance scorecards.
Enable a marketplace of agents/services with productized APIs, documentation, chargeback, and KPIs.
Responsible AI, Security & Compliance
Implement multi-layer guardrails: policy prompts, filters, memory governance, tool whitelisting, audit logs; ensure regulator-ready posture.
Codify privacy, PII handling, data residency, and per-tenant isolation using VPC-SC, Secret Manager, IAM, and Apigee policies.
Leadership & Enablement
Mentor senior engineers and team leads; run architecture reviews, design clinics, and red-team exercises.
Drive continuous evaluation programs and publish org scorecards for quality, safety, and cost.
Partner with Product, Security, and SRE to align roadmaps, SLOs, and operational playbooks.
Required Technical Competencies
Dataflow and Apache Beam for data pipeline development.
Strong on using SQL for data analysis.
LLM & GenAI: Model selection (Gemini & Model Garden), prompt engineering, RAG/grounding, multimodal pipelines, fine-tuning/adapter methods.
Agentic AI (ADK): Agent loops, planners, tool/function design, memory (episodic/semantic/long-term), HIL, policy enforcement.
Data & Retrieval: BigQuery (including vector functions), Vertex Vector Search, Document AI, Dataplex for lineage and governance.
Orchestration & Services: Cloud Run, Workflows, Pub/Sub, Dataflow/Composer; HA/DR, backpressure, circuit breakers.
LLMOps/MLOps: Vertex AI Pipelines, registry, CI/CD, trace correlation, cost/performance monitoring.
Security & Compliance: IAM, Secret Manager, VPC-SC, private service connect, DLP, Okta/IAP, Apigee API policies.
Observability & Cost: Central telemetry, user feedback loops, drift/outlier detection, quota/capacity planning.
Qualifications
12–15+ years in software/data/ML engineering; 1+ years hands-on with LLMs/GenAI and agentic systems.
Proven delivery of enterprise-scale GenAI/agent platforms on GCP (Vertex AI, BigQuery, Cloud Run, Pub/Sub, Workflows).
Demonstrated impact in platformization, governance, and multi-team technical leadership.
Strong proficiency in Java.
Strong proficiency in Python/TypeScript (or equivalent) and infrastructure-as-code (Terraform/GCP Deployment Manager).
Experience in security-by-design, privacy, and compliance audits.
Proven delivery in building data pipeline using distributed computing frameworks such as Dataflow, Spark.
We will give careful consideration to your application and review your details against the position criteria. You will receive separate notification as your application progresses.
Please note that only candidates who meet the minimum criteria for the role will proceed in the selection process.
#LI-Hybrid#LI-GS1Synechron · Gurugram