Cenna Systems

Agent-powered IT service automation platform

Founding Engineer - AI

AI Application EngineeringConfirmed live in the last 24 hours

Full-timeHyderabad, Telangana, IndiaNo salary listedIn-person

About the Job

Requirements

The candidate must have 3–5 years of experience in software engineering, including at least 1–2 years focused on artificial intelligence, machine learning, or large language model applications.
The candidate must have strong programming skills in Python.
The candidate must have hands-on experience with agent frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Haystack, or similar frameworks.
The candidate must have experience building applications with large language model application programming interfaces, including OpenAI, Anthropic, Azure OpenAI, or open-source models.
The candidate must understand prompt engineering, chain-of-thought reasoning, and tool-use patterns.
The candidate must be familiar with retrieval-augmented generation architectures, vector databases, and embedding models.
The candidate must be able to debug complex artificial intelligence systems and iterate based on real-world failures.
The candidate must have strong communication skills and be able to work cross-functionally.

Responsibilities

Build the agentic services platform from zero to one.
Own backend architecture from initial concept through production deployment and enterprise scale.
Design and build multi-agent systems that orchestrate complex business process automation.
Work with agent frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks to build reliable, production-grade agentic workflows.
Implement and optimize large-language-model-powered reasoning, planning, and tool-use capabilities.
Build retrieval-augmented generation pipelines and knowledge retrieval systems to ground agents in enterprise context.
Fine-tune and evaluate Small Language Models for domain-specific tasks.
Develop prompt engineering strategies and evaluation frameworks for agent reliability.
Collaborate with integration engineers to connect agents with enterprise systems.
Debug and improve agent behavior based on production feedback and failure analysis.

Desired Qualifications

Experience fine-tuning large language models or training custom models using methods such as LoRA or PEFT.
Familiarity with browser/computer-use agents and user-interface automation.
Experience with machine-learning infrastructure, including model serving, graphics processing unit optimization, and inference at scale.
A background in reinforcement learning or decision-making systems.
An understanding of enterprise information technology, information technology service management, or business process automation.
Startup experience or comfort working in fast-paced, ambiguous environments.

Build our agentic services platform from zero to one. Own backend architecture from initial concept through production deployment to enterprise scale.

About this role

We're looking for an AI Engineer to build and improve the intelligent core of our agentic platform. You'll work on multi-agent systems, LLM orchestration, and the AI capabilities that power our enterprise automation. This is a hands-on role where you'll design, implement, and optimize AI systems that execute real business processes — not just chatbots, but agents that get work done.

Responsibilities

Design and build multi-agent systems that orchestrate complex business process automation
Work with agent frameworks (LangChain, LangGraph, CrewAI, AutoGen, or similar) to build reliable, production-grade agentic workflows
Implement and optimize LLM-powered reasoning, planning, and tool-use capabilities
Build RAG pipelines and knowledge retrieval systems to ground agents in enterprise context
Fine-tune and evaluate Small Language Models (SLMs) for domain-specific tasks
Develop prompt engineering strategies and evaluation frameworks for agent reliability
Collaborate with integration engineers to connect agents with enterprise systems
Debug and improve agent behavior based on production feedback and failure analysis

Requirements

3-5 years of experience in software engineering, with at least 1-2 years focused on AI/ML or LLM applications
Strong programming skills in Python
Hands-on experience with agent frameworks: LangChain, LangGraph, CrewAI, AutoGen, Haystack, or similar
Experience building applications with LLM APIs (OpenAI, Anthropic, Azure OpenAI, open-source models)
Understanding of prompt engineering, chain-of-thought reasoning, and tool-use patterns
Familiarity with RAG architectures, vector databases, and embedding models
Ability to debug complex AI systems and iterate based on real-world failures
Strong communication skills and ability to work cross-functionally

Nice to Have

Experience fine-tuning LLMs or training custom models (LoRA, PEFT, etc.)
Familiarity with browser/computer-use agents and UI automation
Experience with ML infrastructure: model serving, GPU optimization, inference at scale
Background in reinforcement learning or decision-making systems
Understanding of enterprise IT, ITSM, or business process automation
Startup experience or comfort working in fast-paced, ambiguous environments

If you're excited about building AI agents that automate real work, we'd love to hear from you. Apply with your resume and tell us about an interesting agentic system you've built or a hard problem you've solved with LLMs.

About the Company

Cenna Systems

Agent-powered IT service automation platform

Cenna Systems builds an enterprise IT automation platform that unifies IT service management and IT operations using agent-powered automation and real-time orchestration. It automates tasks across infrastructure, security, and compliance with lightweight multi-agent systems and domain-specific language models, and it captures interactions to build a continuously evolving knowledge graph for better visibility. Unlike traditional ticket-based ITSM, it relies on autonomous agents and an evolving knowledge base to drive actions, with containerized deployment and security tool integrations for enterprise use. Its goal is to help enterprise IT teams operate more efficiently by delivering end-to-end automation and real-time insights for proactive, informed IT service delivery.

Company size: Not listed
Company stage: Not listed
Total funding: Not listed
Headquarters: San Francisco, CA
Founded: 2025

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