Founding AI Infrastructure Engineer

About REM Labs

REM is building an autonomous software delivery platform that runs software delivery end to end with enterprise-grade reliability, control, and governance.

As AI systems begin participating directly in engineering workflows, the hard problem is no longer generation. It is enabling autonomous systems to execute reliably across complex enterprise environments while maintaining control, observability, traceability, and safe behavior.

We are building the infrastructure layer that makes this possible.

Role Overview

We are hiring a Founding AI Infrastructure Engineer to help design and build the AI systems infrastructure powering REM.

This is a deeply technical founding role focused on AI execution systems, context infrastructure, model interaction, evaluation, reliability, and runtime behavior for autonomous engineering workflows.

You will work directly with the founder and early engineering team to define how autonomous AI systems interact safely and reliably with enterprise engineering environments.

This is not a prompt engineering role. We are looking for engineers who combine strong backend/platform fundamentals with meaningful experience building production AI systems.

What You Will Work On

Agent Execution Infrastructure

Design systems that enable AI agents to execute complex engineering tasks safely across tools, APIs, and enterprise systems.

Context, Retrieval, and Memory

Build infrastructure for retrieval, context propagation, memory persistence, and knowledge grounding across long-running workflows.

Tool Interaction Frameworks

Design reliable interfaces between AI systems and external platforms such as Git, CI/CD, issue trackers, cloud infrastructure, and internal execution services.

Model Infrastructure

Build abstractions around model interaction, inference management, structured outputs, streaming execution, fallback handling, and model routing.

Evaluation and Reliability

Build systems to evaluate agent behavior, execution quality, retrieval quality, regression detection, and runtime reliability.

Observability and Guardrails

Design execution visibility, debugging systems, validation layers, policy enforcement, and operational safeguards for autonomous systems.

Platform Foundations

Help shape architecture, engineering standards, abstractions, and the long-term technical direction of REM's AI infrastructure.

What We Are Looking For

Typically 6–12 years of engineering experience across backend systems, platform engineering, infrastructure, or AI systems, with meaningful hands-on exposure to production AI/LLM systems.

Strong candidates typically have experience in several of the following:

• AI platform engineering
• LLM / GenAI infrastructure
• retrieval / RAG systems
• model serving / inference infrastructure
• backend or distributed systems engineering
• evaluation / observability for AI systems
• developer platforms or execution frameworks

You should be comfortable operating in ambiguous early-stage environments and thinking in systems rather than isolated features.

What Success Looks Like

In the first few months, success means:

• shaping REM's AI infrastructure architecture
• improving how agents retrieve, maintain, and use context
• building reliable model and execution infrastructure
• influencing core technical decisions across the platform
• collaborating closely with the founding engineering team
• participating in technical diligence and customer architecture discussions where needed

Over time, this role is expected to evolve into a major technical leadership position.

Team Culture

We value:

• first-principles thinking
• strong engineering judgement
• wicket smart energy
• high agency
• low ego collaboration
• building durable systems over short-term hacks

This is a founding-stage environment. Problems are ambiguous, constraints evolve quickly, and many patterns do not yet exist.

Location

Bangalore

Compensation

• competitive cash compensation
• meaningful founding-stage equity
• significant technical ownership and long-term influence