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Sr AI Engineer, Agentic Systems

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Sr AI Engineer, Agentic Systems in Others new

  • Bengaluru, India

Responsibilities

  • Lead product discovery with Product Managers and domain experts to map workflows, define problems, establish baselines, and set success criteria.
  • Design, build, test, deploy, and operate production agents, automations, and internal tools using coding agents, LLM APIs, and agent frameworks.
  • Develop multi-agent workflows, orchestration, tools, context and state management, caching, retries, failure handling, and reliable autonomy.
  • Build full-stack systems including backend services, APIs, integrations, data models, approval UIs, dashboards, chat interfaces, and other user-facing surfaces.
  • Apply version control, code review, automated testing, CI or CD, staged rollouts, rollbacks, logging, metrics, and tracing to AI systems.
  • Create evaluations, quality gates, defensive guardrails, safety mechanisms, and human-in-the-loop approval processes, then monitor and improve production runs.
  • Use measured business results to evolve or retire agents and turn successful systems into reusable patterns and libraries.

Requirements

  • Around 7 or more years of professional software engineering experience in production systems.
  • Around 2 or more years of professional, hands-on experience building AI agents and agent-powered production applications with measurable outcomes.
  • Strong full-stack experience designing, building, and owning services end to end, including backend APIs, integrations, data models, and user-facing interfaces.
  • Experience with system design, APIs, data models, latency, throughput, failure modes, algorithms, data structures, access-control permissions, and data provenance.
  • Ability to build agents in code using LLM APIs, SDKs, and libraries and to design reliable multi-agent orchestrations for real users and real-world data.
  • Experience with continuous evaluation, safety mechanisms, guardrails, production monitoring, test cases derived from failures, and data-driven tradeoffs involving models, latency, cost, context, and autonomy.
  • Preferred: knowledge or context graphs, entities and relationships, GraphRAG, hybrid retrieval, Google ADK, LangGraph, OpenAI-style systems, cloud or container platforms, Python, FastAPI or similar stacks, MCP, Slack or Teams bots, REST APIs, webhooks, Snowflake, Salesforce, graph stores, conversational flows, chat surfaces, guided forms, and dashboards.

Rubrik

Rubrik (NYSE: RBRK), the Security and AI company, operates at the intersection of data protection, cyber resilience and enterprise AI acceleration. The Rubrik Security Cloud platform is designed to deliver robust cyber resilience and recovery including identity resilience to ensure continuous bus...

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