Principal GenAI & Agentic AI Engineer

Sabre; Bengaluru; Hybride; INR 4 000 000 - 7 000 000; Plein temps

Résumé du poste

Sabre is seeking a Principal GenAI & Agentic AI Engineer to lead the design, development, and scaling of AI systems that combine LLM-powered GenAI and ADK-based agentic workflows on Google Cloud Platform. You will set architecture standards, drive multi‑team delivery, and govern safety, reliability, and cost at enterprise scale to monetize AI products.

You will mentor engineers, shape platformization, and collaborate with product, security, and SRE to align roadmaps, SLOs, and operational

Qualifications

• 12–15+ years in software/data/ML engineering.
• 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).
• Strong proficiency in Java and Python/TypeScript.
• Experience in security-by-design, privacy, and compliance audits.

Responsabilités

• Define reference architectures for GenAI apps, RAG systems, and agent ecosystems on GCP.
• Lead multi-team delivery with focus on reliability, safety, and cost control.
• Architect and deliver production‑grade GenAI solutions and agent services.
• Standardize LLMOps and promote reusable patterns across the organization.
• Drive data pipelines using Dataflow/Composer and BigQuery vectors for retrieval.

Connaissances

Java

Python/TypeScript

GenAI/LLM

GCP Vertex AI

Data pipelines

Security & Compliance

Formation

Bachelor's degree

Outils

Terraform

Deployment Manager

Okta/IAP

Apigee

BigQuery

Dataflow/Composer

Description du poste

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.