IND Staff Software Engineer

External

Thehartford · Gcc-puppalaguda Village, India

Full-timeHybridToday

AuditingAWSCI/CDComplianceGCPGitHub

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About the role

The Senior AI Engineer will architect, build, and operationalize advanced AI and multi-agent solutions leveraging RAG , GraphRAG , Agentic AI frameworks , and enterprise ‑ grade 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 knowledge ‑ graph -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

Multi ‑ step 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 layers ‑ compliant 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 design ‑ privilege design

Secure MCP tool access, command permissioning , and input validation

Architect safe session ‑ based 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 cross ‑ domain AI capabilities.

Document architecture patterns, reusable code modules, and standards for MCP/Agentic development.

IND Staff Software Engineer

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Company Intel

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