Full Stack AI Engineer

We're looking for a Full Stack AI Engineer to own complete AI-native systems from concept through production at a fast-moving enterprise AI platform. This means product thinking, UX, frontend, backend, AI workflows, infrastructure, deployment, and iteration — all of it, not a slice of it.

This is not a narrow frontend, backend, or machine learning role. You will take loosely defined business problems, design the user experience, decide how the system should work, build every layer, deploy it securely, measure whether it works, and improve it based on real usage. The hardest thing to find for this role is not technical skill — it is systems thinking. If you think beyond individual tickets, consider second-order effects before making technical decisions, and prefer ownership and ambiguity over narrowly scoped work, this role is a strong fit.

Why You'll Want to Join

You will be paid in USD (bi-monthly: every 15th and 30th)

Paid Time Off in accordance with company policy

Observance of Holidays per company guidelines

100% remote setup so you can work wherever you're most productive

This role requires availability during US business hours

Own large parts of the product and architecture at an early, high-leverage stage

Work directly with enterprise customers and see the real impact of what you build

Help establish the engineering foundation of a company building AI infrastructure that influences real decisions

What You'll Work On

End-to-End Product and System Ownership

Build complete AI-native products from concept through production

Turn loosely defined business problems into clear product and technical systems

Design polished, simple interfaces for complex enterprise workflows

Zoom in to debug a broken API call and zoom out to question whether the entire workflow is designed correctly

Make architecture decisions based on scale, reliability, security, speed, and long-term maintainability

Frontend and Backend Development

Build responsive frontend applications and reusable component systems using React and Next.js

Build secure APIs, backend services, data pipelines, databases, and asynchronous workflows

Design systems for multi-tenancy, role-based access, auditability, data isolation, and enterprise security

Connect the platform to enterprise systems including HRIS, ATS, CRM, email, calendars, and internal databases

AI and LLM Engineering

Develop LLM-powered features, agents, retrieval systems, evaluation pipelines, and decision workflows

Build production AI systems with strong understanding of RAG, embeddings, tool calling, structured outputs, agent workflows, prompt design, model evaluation, and failure handling

Design systems that operate inside customer VPCs, private clouds, and controlled enterprise environments

Use AI coding tools aggressively to move faster without allowing generated code to reduce quality or understanding

Infrastructure and Observability

Deploy, monitor, and maintain cloud-based applications across GCP, AWS, Azure, and customer VPC environments

Build observability, testing, logging, alerting, and evaluation systems so issues are caught before customers notice

Design for CI/CD, infrastructure as code, and secure deployment pipelines

Own authentication, permissions, encryption, secrets management, and data privacy across the stack

What You Bring

5 or more years of professional software engineering experience with a track record of building and shipping real production systems, ideally from zero to meaningful scale

Strong frontend experience with React, Next.js, and TypeScript

Strong backend experience with Python, TypeScript, Node.js, or similar technologies

Hands-on experience building production AI or LLM features, not just prototypes or API wrappers

Deep understanding of RAG, agents, tool use, memory systems, context management, and evaluation frameworks

Experience designing APIs, databases, data models, queues, event-driven systems, and distributed workflows

Strong product judgment and enough design taste to turn complicated workflows into interfaces that feel obvious

Strong understanding of authentication, permissions, encryption, secrets, data privacy, and secure software design

High ownership — you do not wait for someone else to define every requirement, catch every edge case, or clean up after launch

Clear written and verbal communication in an async, distributed environment

Nice to Have

Experience with PostgreSQL, Redis, vector databases, and search systems

Familiarity with OAuth, SSO, SAML, SCIM, RBAC, audit logs, and enterprise integrations

Experience with Docker, Kubernetes, and infrastructure as code

Background deploying inside customer VPCs or private networking environments

Experience with Figma or the ability to independently create strong product flows and interfaces

Prior experience as a founding engineer or primary engineer at an early-stage company

How to Apply

Please include:

Your updated resume

A portfolio link or GitHub showing AI systems or full stack projects you have built and shipped in production

A short Loom video (1 to 2 minutes) introducing yourself, walking through a system you are most proud of, and explaining the architectural decisions you made — with a focus on how you thought about the system as a whole, not just individual components

Only candidates who submit both a portfolio and Loom video will be moved to the next step of the hiring process.

If you are a full stack AI engineer who thinks in systems, can own the entire product lifecycle independently, and wants to build AI infrastructure that influences real decisions at enterprise scale, this role gives you the ownership and the direct impact to do your best work.

Application Process Overview

Our comprehensive selection process ensures we find the right fit for both you and our clients:

Initial Application - Submit your application and complete our prequalifying questions

Video Introduction - Record an video introduction to showcase your communication skills and work experience

Role-Specific Assessment - Complete a homework assignment tailored to the position (if applicable)

Recruitment Interview - Initial screening with our talent team

Executive Interview - Meet with senior leadership to discuss role alignment

Client Interview - Final interview with the client team you'd be supporting

Background & Reference Check - Professional reference verification

Job Offer - Successful candidates receive a formal offer to join the team

Each stage is designed to evaluate your fit for the role while giving you insights into our company culture and expectations. We'll keep you informed throughout the process and provide feedback at each step.

Originally posted on Himalayas