[Hiring] Founding Head of Engineering @rpsarathy

Role Description

We're looking for a Founding Head of Engineering to partner directly with the founders, build the platform from the ground up, and establish the engineering team and culture.

This is a hands-on technical leadership role for someone who enjoys building as much as leading. You'll own the architecture, write production code, ship early product releases, and recruit the engineers who will help scale AgentFloor.

Strong practical experience with retrieval-augmented generation (RAG), LLM-powered applications, and backend systems is required. You should be comfortable moving from an early prototype to a secure, reliable product that supports both startup and enterprise customers.

What you'll do

• Build the product and platform
  • Own the technical architecture, engineering roadmap, and end-to-end delivery.
  • Build the core platform for configuring, deploying, managing, and monitoring AI agents.
  • Develop APIs, backend services, data pipelines, and integrations with customer systems.
  • Work directly with founders and early customers to turn business needs into product capabilities.
  • Make pragmatic build-versus-buy decisions and balance delivery speed with maintainability.
• Own RAG and AI engineering
  • Design and implement RAG pipelines covering document ingestion, parsing, chunking, embeddings, indexing, retrieval, and reranking.
  • Build knowledge integrations that connect agents to relevant business information while respecting access permissions.
  • Develop agent workflows involving tool calling, orchestration, state management, and human approval.
  • Establish evaluation workflows for retrieval relevance, response accuracy, groundedness, and task completion.
  • Measure and improve latency, reliability, token usage, and inference costs.
  • Implement safeguards for prompt injection, sensitive-data exposure, and unauthorized tool execution.
• Establish production foundations
  • Design secure, multi-tenant architecture with appropriate data isolation and access controls.
  • Own cloud infrastructure, CI/CD, automated testing, observability, and deployment practices.
  • Build monitoring and tracing across retrieval, model calls, agent actions, and external integrations.
  • Establish practical standards for code quality, incident response, and technical documentation.
  • Evolve the platform as customer usage and enterprise requirements grow.
• Build and lead the team
  • Recruit, mentor, and lead the initial engineering team.
  • Create a culture of ownership, clear communication, and fast, disciplined execution.
  • Set engineering priorities and communicate technical trade-offs to founders and stakeholders.
  • Remain an active contributor to implementation, code reviews, and technical problem-solving.

Qualifications

• A track record of building and shipping production software, with ownership of architecture and delivery.
• Direct experience building RAG applications beyond tutorials, demos, or proof-of-concept projects.
• Strong Python skills and experience developing backend services, APIs, and data pipelines.
• Experience with embeddings, vector databases or search engines, semantic search, metadata filtering, and retrieval evaluation.
• Practical experience integrating LLM APIs and building reliable AI-powered workflows.
• Strong foundations in system design, databases, distributed systems, and cloud infrastructure.
• An understanding of authentication, authorization, tenant isolation, and secure handling of customer data.
• Experience leading technical projects, mentoring engineers, and making architectural decisions.
• The ability to work through ambiguity, prioritize effectively, and deliver with a small team.
• Clear communication skills and comfort collaborating with customers and nontechnical stakeholders.

Nice to have

• Experience as a technical founder, founding engineer, or early-stage engineering leader.
• Experience building AI-agent platforms or enterprise AI products.
• Familiarity with agent orchestration frameworks such as LangGraph.
• Experience with Model Context Protocol (MCP), tool integrations, and agent authorization.
• Experience processing complex documents, including PDFs, scanned files, tables, and enterprise knowledge sources.
• Experience with hybrid search, reranking, knowledge graphs, or multimodal retrieval.
• Familiarity with frontend development using React or a similar framework.
• Experience supporting enterprise security reviews, audit logging, SSO, and compliance readiness.
• Experience optimizing model selection, caching, and inference costs.

What success looks like

• First 30 days
  • Align with the founders on customer needs, product priorities, and technical direction.
  • Assess the existing product and codebase, if applicable.
  • Define the initial architecture, delivery milestones, and key engineering risks.
  • Begin shipping improvements to the core product.
• First 60 days
  • Deliver an end-to-end agent workflow connected to customer knowledge and tools.
  • Establish baseline evaluations for RAG and agent performance.
  • Put essential testing, deployment, monitoring, and security controls in place.
  • Define the initial engineering hiring plan.
• First 90 days
  • Deliver a usable early product release aligned with customer priorities.
  • Establish measurable baselines for quality, reliability, latency, and cost.
  • Create a repeatable engineering delivery process.
  • Begin building the team needed for the next stage of growth.

Why join AgentFloor?

• Shape the product, architecture, and engineering culture from the beginning.
• Work directly with the founders and early customers.
• Own meaningful technical and product decisions.
• Build across AI agents, RAG, orchestration, and enterprise infrastructure.
• Receive competitive compensation and meaningful founding-team equity.

How to apply

Send your résumé or LinkedIn profile to [email/application link]. Include a short description of:

• A RAG or AI-agent system you shipped.
• Your specific technical and leadership contributions.
• A challenging architectural trade-off you made and why.
• Any relevant GitHub repositories, demos, or technical writing you can share.

We value demonstrated ability, sound judgment, and ownership—not just job titles or familiarity with a particular framework.