We’re hiring an experienced AI-native engineer to build reliable, production-grade AI agent systems at Diligent. This is a software engineering role focused on designing and building the core AI harness, including prompt and context engineering, tool integration, agent orchestration, memory, evaluations, model routing, observability, and feedback loops.. 

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.

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.

Interview Focus

• Backend engineering and system design.
• LLM/agent workflow experience beyond simple prompts.
• Prompt engineering, context engineering, and harness engineering depth.
• Ability to design evals, feedback loops, and observability for AI systems.
• Ability to decompose complex workflows into reliable technical components.
• Product thinking, ownership, and ability to work in ambiguity.