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.