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 memory Session and context lifecycle management Tooling interfaces Secure capability boundaries Permissions and role enforcement Additionally, candidates must have hands-on experience with AlloyDB's AI/Agentic capabilities —including vector indexing, embedding support, and tight integration with Vertex AI—as 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 knowledgegraph–augmented 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 state Tool authorization Multistep workflows and orchestration Session boundary and identity controls Leverage AlloyDB and PostgreSQL/RDS for: Vector storage and hybrid search Agent memory persistence, session management, and state recovery Structured prompt scaffolding and fact retrieval ACID compliant transactional reasoning layerscompliant transactional reasoning layers Develop 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 flows IAM roles, service accounts, least privilege designprivilege design Secure MCP tool access, command permissioning, and input validation Architect 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 6–8 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, security Google ADK Agentic Protocols — agents, workflows, context management Databases & Agent Memory Stores Handson experience with AlloyDB , including: Vector indexing / pgvector AI inference acceleration and Vertex AI integration Building agent memory and retrieval layers Transactional context management for Agentic systems Strong PostgreSQL/Postgres RDS fundamentals , including: Schema design for knowledge retrieval Query optimization Hybrid search patterns Durable storage for AI session and memory state Cloud & Platform Skills Experience with: Vertex AI (Model Garden, Embeddings, Vector Search, Generative AI APIs) GCP Cloud Run, AlloyDB, Cloud Storage, Secret Manager Terraform / IaC CI/CD automation, containerization, environment provisioning OAuth, SSO, IAM roles/policies, service account management Additional Experience with AI coding tools (Claude Code, GitHub Copilot, AWS Kiro). Strong understanding of LLM safety, governance, context window management, and prompt engineering. Preferred Certifications GCP Professional Cloud Architect GCP Professional Machine Learning Engineer Education Bachelor's or Master's in Computer Science, Engineering, or related field.