Platform & Agentic AI Engineer

Overview This requisition hires Senior AI Engineers who will: Design and deliver productiongrade Agentic AI systems using Google ADK, Anthropic MCP, LangGraph/LangChain, and modern Agentic protocols.Build secure, scalable AI platform capabilities with strong engineering fundamentals in Python/Typescript, Terraform, and GCP.Enable enterprise adoption of AI by creating reusable frameworks, APIs, and platform capabilities aligned with engineering standards, compliance needs, and modern cloud patterns. Overview The Senior AI Engineer will architect, build, and operationalize advanced AI and multi-agent solutions leveraging RAG, GraphRAG, Agentic AI frameworks, and enterprisegrade cloud engineering. A key requirement is robust, practical experience implementing MCP and ADK Agentic Protocols, with a solid understanding of: Agent memorySession and context lifecycle managementTooling interfacesSecure capability boundariesPermissions and role enforcementAdditionally, candidates must have hands-on experience with AlloyDB's AI/Agentic capabilitiesincluding vector indexing, embedding support, and tight integration with Vertex AIas well as strong fundamentals in PostgreSQL / Postgres RDS for building retrieval systems, agent memory stores, and structured context-management layers. The engineer must demonstrate strong foundational engineering skills in Python or Typescript, IaC (Terraform), DevOps pipelines, and secure distributed system design using GCP services such as Vertex AI, Cloud Run, Cloud Storage, and AlloyDB. Responsibilities AI/Agentic System Architecture & Development Design and implement Agentic AI solutions using Google ADK, LangGraph, LangChain, and Agent Engine.Build advanced RAG and GraphRAG pipelines, vector retrieval systems, and knowledgegraphaugmented reasoning.Implement MCP-compliant agents with capability registration, secure tool invocation, memory storage, and session state management. Apply deep knowledge of Agentic Protocol design (ADK & MCP), such as:Agent memory and conversation stateTool authorizationMultistep workflows and orchestrationSession boundary and identity controlsLeverage AlloyDB and PostgreSQL/RDS for:Vector storage and hybrid searchAgent memory persistence, session management, and state recoveryStructured prompt scaffolding and fact retrievalACID compliant transactional reasoning layerscompliant transactional reasoning layersDevelop scalable AI microservices using Python/Typescript, Cloud Run, Vertex AI, and event-driven components.Optimize model inference, retrieval latency, and overall system performance. Security, Governance & Session Management Implement enterprise-grade security for agents including:OAuth and SSO flowsIAM roles, service accounts, least privilege designprivilege designSecure MCP tool access, command permissioning, and input validationArchitect safe sessionbased AI interactions with proper expiration, auditing, and context isolation.Ensure compliance with enterprise governance, Responsible AI requirements, and platform guardrails. Platform Engineering, IaC & DevOps Use Terraform to build GCP infrastructure for AI workloads, vector stores, knowledge graphs, and orchestration services.Build CI/CD pipelines for model deployments and agent lifecycle automation.Implement observability, monitoring, and logging for AI service health. Innovation & Collaboration Evaluate emerging tools like Claude Code, GitHub Copilot, AWS Kiro and integrate them into engineering workflows.Partner with architects, data engineers, and platform teams to implement crossdomain AI capabilities.Document architecture patterns, reusable code modules, and standards for MCP/Agentic development. Qualifications Experience 68 years in software engineering, including 2+ years in GenAI, multi-agent, or LLM systems.Proven delivery of at least one productiongrade AI or Agentic system, preferably involving RAG or GraphRAG. Technical Expertise Core Engineering Strong engineering fundamentals in Python and/or Typescript.Agentic AI & Protocols Deep, practical experience with:MCP (Model Context Protocol) tools, capabilities, memory, session orchestration, securityGoogle ADK Agentic Protocols agents, workflows, context managementDatabases & Agent Memory Stores Handson experience with AlloyDB, including:Vector indexing / pgvectorAI inference acceleration and Vertex AI integrationBuilding agent memory and retrieval layersTransactional context management for Agentic systemsStrong PostgreSQL/Postgres RDS fundamentals, including:Schema design for knowledge retrievalQuery optimizationHybrid search patternsDurable storage for AI session and memory stateCloud & Platform Skills Experience with:Vertex AI (Model Garden, Embeddings, Vector Search, Generative AI APIs)GCP Cloud Run, AlloyDB, Cloud Storage, Secret ManagerTerraform / IaCCI/CD automation, containerization, environment provisioningOAuth, SSO, IAM roles/policies, service account managementAdditional Experience with AI .