Google Cloud Platform LLM Agentic AI Solution Architect

Educational Qualification*

Bachelor's or Master's degree in Computer Science, Data Science, or a related field.

Experience Range

10-12 years of experience in AI/ML-related roles, with a strong focus on LLM's & Agentic AI technology.

Primary (Must have skills)* - To be Screened by TA Team

Generative AI Solution Architecture (2–3 years): Proven experience in designing and architecting GenAI applications, including Retrieval-Augmented Generation (RAG), LLM orchestration (LangChain, LangGraph), and advanced prompt design strategies.

Backend & Integration Expertise (5+ years): Strong background in architecting Python-based microservices, APIs, and orchestration layers that enable tool invocation, context management, and task decomposition across cloud-native environments (Azure Functions, Google Cloud Platform Cloud Functions, Kubernetes).

Enterprise LLM Architecture (2–3 years): Hands-on experience in architecting end-to-end LLM solutions using Azure OpenAI, Azure AI Studio, Hugging Face models, and Google Cloud Platform Vertex AI, ensuring scalability, security, and performance.

Expertise in designing and optimizing RAG pipelines, including enterprise data ingestion, embedding generation, and vector search using Azure Cognitive Search, Pinecone RAG & Data Pipeline Design (2–3 years):, Weaviate, FAISS, or Google Cloud Platform Vertex AI Matching Engine.

LLM Optimization & Adaptation (2–3 years): Experience in implementing fine-tuning and parameter-efficient tuning approaches (LoRA, QLoRA, PEFT) and integrating memory modules (long-term, short-term, episodic) to enhance agent intelligence.

Multi-Agent Orchestration (2–3 years): Skilled in designing multi-agent frameworks and orchestration pipelines with LangChain, AutoGen, or DSPy, enabling goal-driven planning, task decomposition, and tool/API invocation.

Performance Engineering (2–3 years): Experience in optimizing Google Cloud Platform Vertex AI models for latency, throughput, and scalability in enterprise-grade deployments.

AI Application Integration (2–3 years): Proven ability to integrate OpenAI and third-party models into enterprise applications via APIs and custom connectors (MuleSoft, Apigee, Azure APIM).

Governance & Guardrails (1–2 years): Hands-on experience in implementing security, compliance, and governance frameworks for LLM-based applications, including content moderation, data protection, and responsible AI guardrails.

Job Description of Role* (RNR) - To be Evaluated by Technical Panel (Define it to give more clarity)

Key technical skills :

As a Technical Architect specializing in LLMs and Agentic AI, you will own the architecture, strategy, and delivery of enterprise-grade AI solutions. You will work with cross-functional teams and customers to define the AI roadmap, design scalable solutions, and ensure responsible deployment of Generative AI across the organization:

• Agile Methodologies: Experience working in Agile development environments.