We're hiring an experienced AI-native engineer to build the core systems behind Diligent's AI agents. This is not a prompt-writing role. This is a software engineering role for someone who can design reliable AI systems: prompts, context, tools, evals, orchestration, memory, observability, and production workflows.

About Us

Diligent is the current venture from the founding team of Leverage Edu, a Series D edtech company. There, our founders built and scaled multiple businesses from zero, including student mobility, cross-border payments and student housing.

Diligent is building the AI growth team every business deserves. Instead of hiring an agency or a five-person marketing team, our clients get a squad of AI agents that research competitors, plan budgets, generate creatives, and run and optimise campaigns on Meta and Google, with senior marketers guiding every move. A campaign that takes an agency weeks to set up goes live in under 30 minutes.

We're a small team building fast. Everyone here uses AI every day, and you will too.

The Role

You'll work on the core agent architecture behind Diligent.

Your job will be to build the harness around frontier models: prompt engineering, context engineering, tool design, agent orchestration, evals, memory, model routing, feedback loops, and human-in-the-loop systems.

You should be equally comfortable thinking about system design and writing production code.

What You'll Do

- Design and build AI agent workflows for marketing, growth, reporting, creative analysis, SEO/AEO, lead intelligence, and reputation monitoring.
- Build production-grade harnesses around LLMs using prompts, context, tools, memory, structured outputs, evals, guardrails, and human review loops.
- Work on prompt engineering, context engineering, tool calling, workflow decomposition, and agent orchestration.
- Build backend systems that connect with databases, APIs, CRMs, ad platforms, Slack, WhatsApp, dashboards, and internal tools.




- Design systems for ingesting, cleaning, storing, and retrieving business context for AI agents.
- Build evaluation pipelines to test prompt changes, model changes, agent quality, regressions, and edge cases.
- Implement model routing strategies to balance quality, latency, and cost. Improve observability: logs, traces, prompt versions, model outputs, failure analysis, and feedback loops.
- Work closely with product and growth teams to convert messy business workflows into reliable AI systems. Own engineering quality across architecture, code, testing, debugging, deployment, and maintenance.

Who You Are

Must-haves

- 5+ years of software engineering experience.
- Strong backend engineering and system design fundamentals.
- Hands-on experience building with LLMs, AI agents, prompt engineering, context engineering, RAG, tool calling, or agent frameworks.
- Strong software development fundamentals: clean code, APIs, databases, queues, testing, debugging, version control, and deployment.
- Experience with Python, Node.js, TypeScript, or similar backend stacks.
- Comfort working with Postgres, Redis, vector databases, queues, schedulers, and production services.
- Understanding of structured outputs, JSON schemas, retries, validation, error handling, and deterministic workflows.
- Ability to break ambiguous business workflows into clear technical systems.
- High ownership and comfort working in an early-stage startup environment.

Nice-to-haves

- Experience with OpenAI, Anthropic, LangGraph, CrewAI, LlamaIndex, LangChain, MCP, or similar tools.
- Experience building evals for AI systems.
- Experience with tool servers, workflow engines, agent infrastructure, or automation platforms.
- Experience with marketing/adtech/growth tools such as Meta Ads, Google Ads, GA4, HubSpot, Zoho, Shopify, Slack, WhatsApp, or CRMs.
- Experience with AWS, GCP, Azure, Vercel, Render, Railway, or similar cloud platforms.
- Experience with observability, tracing, logging, and production monitoring

📌 AI Harness Engineer (Delhi)
🏢 Diligent OS
📍 Delhi