Randstad Digital
Website:
randstaddigital.com
Company:
https://www.linkedin.com/company/randstaddigital
Seniority: Mid-Senior level
Industries: IT Services and IT Consulting
Job details:
Job Title: GCP AI, GenAI & Agentic AI Architect
Location: India
Role Type: Solution Architecture | AI Platforms | PreSales & Transformation
Role Summary
The GCP AI, GenAI & Agentic AI Architect will be responsible for designing and
shaping enterprisegrade AI, Generative AI, and Agentic AI solutions on Google
Cloud Platform. The role focuses on translating business problems into scalable,
secure, and governable AI architectures, supporting presales solutioning,
architecture definition, and early delivery alignment. The architect acts as a
technical authority across ML platforms, GenAI systems, LLM integration, and
autonomous agent frameworks.
Key Responsibilities
1. AI, GenAI & Agentic Architect
Design endtoend AI and GenAI architectures on GCP, covering data
pipelines, model development, inference, orchestration, and monitoring.
Architect LLMbased applications, including RetrievalAugmented
Generation (RAG), prompt orchestration, multimodel strategies, and
tool/function calling.
Design Agentic AI systems, including taskoriented agents, planners,
toolusing agents, and autonomous workflows.
Define AsIs / ToBe AI architectures, AI modernization roadmaps, and
platform blueprints.
Strong expertise in GCP AI stack (Vertex AI, model training, deployment,
inference)
Gemini Enterprise for Customer Experience (GECX). Create agentic AI
solution using GECX
2. PreSales & Client Advisory
Lead AI/GenAI presales engagements, including discovery workshops,
solution walkthroughs, and executive presentations.
Translate business use cases into practical AI, GenAI, and Agentic AI
solutions with clear value articulation.
Support RFPs, proposals, estimates, and AI platform solution narratives.
3. Technology Leadership
Architect solutions using GCP AI and data services such as Vertex AI,
BigQuery, Dataflow, Dataproc, Cloud Storage, Pub/Sub, and Cloud Run.
Guide LLM integration using Google models and thirdparty LLMs, ensuring
portability and extensibility.
Define model lifecycle management, MLOps, monitoring, and inference
optimization.
4. AI Governance, Risk & Responsible AI
Define guardrails for responsible AI, including bias mitigation, hallucination
control, access controls, and auditability.
Design architectures for model governance, explainability, observability,
and cost control.
Support enterprise frameworks for AI risk management and compliance.
Ensure AI solutions meet security, governance, compliance, and data
privacy requirements.
Required Experience & Skills
Experience
8+ years in data, ML, or cloud architecture roles
3+ years in AI/ML or GenAI solution design
Experience in clientfacing or presales solutioning role
Consulting & Commercial Skills
Ability to articulate business value of AI and GenAI solutions
Strong communication skills with technical and executive stakeholders
Certifications (Preferred)
Google Cloud Professional Machine Learning Engineer
Google Cloud Professional Cloud Architect
GenAIfocused certifications are a plus
Click on Apply to know more.