Ringcentral

Sr Agentic Systems Engineer

India · Work style not stated · Full time

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About the role

Description from the employer’s posting. Check the employer’s page for the current version.

EmployerRingcentral
LocationIndia
Work styleWork style not stated
Employment typeFull time
Employer posted23 Sep 2026

We are looking for an experienced Agentic Systems Engineer who is passionate about designing, building, and productionizing intelligent, autonomous systems powered by LLMs, AI agents, tool use, and modern orchestration frameworks. In this role, you will develop reliable agentic applications, RAG pipelines, and tool integrations within an established platform architecture—connecting AI agents to enterprise systems across multiple business use cases.

Key Responsibilities

Build and ship AI-powered applications using Python and/or Node.js, working within established LLM orchestration frameworks (LangChain, LangGraph, etc.).
Implement agentic workflows — multi-step, tool-using agents that call APIs, vector stores, and databases according to defined design patterns.
Build and tune RAG pipelines for enterprise use cases.
Integrate and extend NLP/NLU models, prompt templates, and embeddings for conversational, classification, and automation tasks.
Build and maintain agent integrations with enterprise applications across multiple business use cases, via REST APIs, connector frameworks, or MCP-style tool registries.
Write and maintain test cases and evaluation harnesses for agent behavior — tool-call accuracy, hallucination rate, and task completion.
Implement evaluation frameworks and observability for LLM/agent systems, including tracing, prompt evaluation, failure analysis, and regression testing.
Apply appropriate guardrails, access controls, validation, and safety mechanisms to ensure agents operate within defined boundaries.
Work with SQL/NoSQL databases to manage structured and unstructured data pipelines feeding agent workflows.
Contribute to full-stack delivery, connecting LLM backends to modern frontends.
Operate within existing access-control, logging, and audit patterns for multi-tool agent systems; flag gaps to the architecture team rather than redesigning independently.

Show full description

Required Skills (Must Have)

6+ years of professional software engineering experience, including at least 1–2 years hands-on building with LLMs or agentic frameworks.
Strong proficiency in Python and/or Node.js for AI and backend development.
Working knowledge of major LLM providers (e.g., OpenAI, Anthropic) and prompt orchestration frameworks (LangChain, LangGraph).
Hands-on expertise in building LLM applications, including
Prompt engineering and enforcing structured outputs
Tool integration and function calling
Retrieval-Augmented Generation (RAG) using vector databases and embedding-based search
Designing multi-agent workflows and orchestration frameworks
Managing context windows and conversational state
Experience integrating external tools and APIs via function calling or tool-registration mechanisms.
Experience integrating agents or tools with enterprise SaaS applications via REST APIs or connector frameworks.
Working knowledge of SQL and NoSQL databases and how they plug into AI pipelines.
Experience writing test cases or evaluation harnesses for agent/LLM behavior (accuracy, hallucination, tool-call correctness).
Familiarity with agent evaluation techniques, including LLM-as-a-judge, task-based evaluations, benchmark design, and automated regression testing.
Understanding of access-control and audit-logging patterns in multi-tool, multi-agent systems.
Understanding of AI security, prompt injection, data leakage, permission boundaries, and agentic-system guardrails.
Strong debugging skills and the ability to implement complex multi-step systems within an established architecture.

Nice to Have Skills

Understanding of cloud deployment and infrastructure (AWS, GCP).
Knowledge of containerization and CI/CD pipelines (Docker, GitHub Actions).
Experience with AI observability, guardrails, and safety mechanisms for LLMs.
Familiarity with data warehouses or analytical query engines for large-scale structured retrieval.

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