Senior AI Solution Architect

Key Responsibilities

• Lead the architecture, design, and implementation of a scalable Agentic AI platform for enterprise use cases.
• Design AI-driven solutions focused on knowledge discovery, reasoning, orchestration, and enterprise automation.
• Build and guide the development of reasoning and orchestration layers using Agentic AI frameworks.
• Transform enterprise services, documents, and business knowledge into searchable APIs and reusable AI capabilities.
• Develop AI solution architecture on Google Cloud Platform (GCP) using cloud-native services and scalable deployment models.
• Lead cloud modernization and migration strategies to enable AI-first enterprise platforms.
• Design secure, scalable, and reusable APIs to expose enterprise knowledge and services to AI agents.
• Provide hands-on technical leadership in Python-based AI engineering, orchestration, and backend service development.
• Evaluate and recommend AI frameworks, vector database platforms, embedding models, and orchestration patterns.
• Partner with product owners, business stakeholders, architects, and engineering teams to convert business needs into AI solution designs.
• Create architecture diagrams, technical design documents, API specifications, integration patterns, and implementation roadmaps.
• Define best practices for responsible AI, security, access control, data privacy, observability, and governance.
• Support proof-of-concepts, MVPs, production deployments, performance tuning, and platform scaling.
• Mentor engineering teams on Agentic AI architecture, cloud-native design, and enterprise AI delivery practices.

Required Skills

• 10+ years of overall IT experience with strong experience in solution architecture, cloud architecture, AI/ML engineering, or enterprise application modernization.
• Strong hands-on experience with Google Cloud Platform (GCP).
• Strong experience designing and implementing Agentic AI frameworks and multi-agent workflows.
• Hands-on experience with Python for AI engineering, backend services, orchestration, and API development.
• Strong experience with vector databases such as Pinecone, Weaviate, Milvus, Chroma, FAISS, Vertex AI Vector Search, or similar platforms.
• Experience building embeddings, semantic search, RAG pipelines, reasoning workflows, and knowledge retrieval systems.
• Strong understanding of LLM-based solution design, prompt engineering, tool calling, function calling, and agent orchestration.
• Experience building AI-driven APIs and transforming enterprise services into searchable, consumable, and reusable AI capabilities.
• Strong understanding of cloud modernization, migration patterns, microservices, APIs, and enterprise integration.
• Experience designing scalable, secure, and production-ready AI platforms.
• Strong knowledge of data pipelines, metadata, document ingestion, chunking strategies, indexing, retrieval optimization, and knowledge governance.
• Able to define architecture standards, reusable design patterns, and technical implementation roadmaps.
• Strong stakeholder management, communication, documentation, and leadership skills.

Preferred Skills

• Experience with GCP services such as Vertex AI, BigQuery, Cloud Run, Cloud Functions, Cloud Storage, Pub/Sub, Cloud SQL, GKE, IAM, and API Gateway.
• Experience with Agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar tools.
• Experience with Knowledge Graphs, ontology design, metadata management, taxonomy design, or enterprise knowledge management.
• Experience designing reasoning/orchestration layers for enterprise GenAI platforms.
• Experience with API management, REST APIs, GraphQL, event-driven architecture, and microservices.
• Experience with MLOps, CI/CD, Docker, Kubernetes, Terraform, and cloud deployment automation.
• Knowledge of security, privacy, governance, and compliance requirements for enterprise AI platforms.

Nice to Have

• Experience with cloud migration and modernization from legacy platforms to GCP.
• Experience implementing AI platforms for enterprise search, knowledge assistants, service automation, or intelligent workflow orchestration.
• Exposure to model evaluation, LLM observability, hallucination controls, guardrails, and prompt/version management.
• Experience working in financial services, healthcare, insurance, retail, or large enterprise environments.

Certifications

• GCP Professional Cloud Architect, Professional Machine Learning Engineer, or related cloud/AI certifications are a plus.