Full Stack Engineer

Location: 100% Remote

Duration: 3 months to start, high likelihood of extension

Full Stack Engineer

OVERVIEW

Seeking a hands-on Full Stack Engineer to design, build, and deliver production-grade platform services for an AI-native enterprise

system. This role sits at the intersection of frontend engineering, backend services, and agentic AI platforms-supporting AI agents and Lang Graph orchestration workflows across the full stack, while ensuring scalable, secure, and observable systems in a cloud-

native environment.

RESPONSIBILITIES

Full Stack Development Backend & API Engineering

Build modern frontend apps (React or Angular,

TypeScript, component architecture)

Develop backend services and APIs using Python

(FastAPI preferred)

Implement secure REST APIs and real-time

communication (WebSockets/streaming)

Ensure clean separation of concerns across

frontend, API, and data layers

Build layered backend architectures using FastAPI and dependency

injection

Implement asynchronous data access patterns and high-performance APIs

Define API contracts, validation models (Pydantic), and error handling

standards

Implement authentication and authorization using JWT, OAuth, IAM

Agentic AI Platform Integration Cloud-Native Development

Build backend services as tools and APIs for

AI agent workflows

Design APIs providing structured, deterministic

data to LLM orchestration layers

Support Human-in-the-Loop (HITL): approvals,

callbacks, state management

Integrate with tool-calling frameworks and external

AI orchestration systems

Enable agent observability via structured logging,

tracing, and correlation IDs

Frontend Engineering

Build responsive, accessible UI components

Implement state management (Redux Toolkit,

Zustand, NgRx, etc.)

Integrate APIs, handle auth flows, and optimize

performance

Write unit and integration tests (Jest, React Testing

Library, Angular equivalents)

Build and deploy services on GCP (preferred), AWS, or Azure

Implement event-driven architectures (Pub/Sub, messaging systems)

Work with managed services for compute, storage, and data processing

Ensure secure configuration, secret management, and scalable deployments

Data Layer & Integration

Design and implement data access across:

Relational databases (PostgreSQL / AlloyDB)

Graph database (Spanner), NoSQL (Firestore)

Caching (Redis) and analytics (BigQuery)

Integrate with enterprise systems (SAP, Microsoft Graph, internal APIs)

Build resilient integrations with retry, timeout, and fault handling

Scope & Expectations

100% hands-on engineering role (no people management)

Collaborate closely with AI/ML engineers on agent workflows

Participate in design discussions, code reviews, and architecture decisions

Deliver production-quality, scalable, and maintainable systems

Testing & Quality Engineering

Write comprehensive tests (pytest, pytest-

asyncio, Jest)

Cover edge cases, failure scenarios, and

asynchronous workflows

Follow best practices for code quality,

observability, and maintainability

SKILLS & EXPERIENCE

Required

8+ years of full stack software engineering experience

Strong frontend experience with React or Angular (TypeScript)

Solid backend development with Python (FastAPI, Flask, or Django)

Production-grade APIs, microservices, and distributed systems

Agentic AI concepts: LangGraph, LangChain, tool-calling workflows

REST APIs, async programming, relational and graph databases

Hands-on with at least one major cloud provider (GCP preferred)

Strong testing practices and Git-based workflows

Preferred

LangGraph or similar agent orchestration frameworks

Human-in-the-Loop (HITL) workflow patterns

Model Context Protocol (MCP) or tool integration patterns

Real-time systems (WebSockets, streaming architectures)

GCP services: Firestore, Pub/Sub, BigQuery, Cloud Run

Distributed caching and concurrency patterns (Redis)

Job Responsibilities

Full Stack Development Backend & API Engineering

Build modern frontend apps (React or Angular,

TypeScript, component architecture)

Develop backend services and APIs using Python

(FastAPI preferred)

Implement secure REST APIs and real-time

communication (WebSockets/streaming)

Ensure clean separation of concerns across

frontend, API, and data layers

Build layered backend architectures using FastAPI and dependency

injection

Implement asynchronous data access patterns and high-performance APIs

Define API contracts, validation models (Pydantic), and error handling

standards

Implement authentication and authorization using JWT, OAuth, IAM

Agentic AI Platform Integration Cloud-Native Development

Build backend services as tools and APIs for

AI agent workflows

Design APIs providing structured, deterministic

data to LLM orchestration layers

Support Human-in-the-Loop (HITL): approvals,

callbacks, state management

Integrate with tool-calling frameworks and external

AI orchestration systems

Enable agent observability via structured logging,

tracing, and correlation IDs

Frontend Engineering

Build responsive, accessible UI components

Implement state management (Redux Toolkit,

Zustand, NgRx, etc.)

Integrate APIs, handle auth flows, and optimize

performance

Write unit and integration tests (Jest, React Testing

Library, Angular equivalents)

Build and deploy services on GCP (preferred), AWS, or Azure

Implement event-driven architectures (Pub/Sub, messaging systems)

Work with managed services for compute, storage, and data processing

Ensure secure configuration, secret management, and scalable deployments

Data Layer & Integration

Design and implement data access across:

Relational databases (PostgreSQL / AlloyDB)

Graph database (Spanner), NoSQL (Firestore)

Caching (Redis) and analytics (BigQuery)

Integrate with enterprise systems (SAP, Microsoft Graph, internal APIs)

Build resilient integrations with retry, timeout, and fault handling

Scope & Expectations

100% hands-on engineering role (no people management)

Collaborate closely with AI/ML engineers on agent workflows

Participate in design discussions, code reviews, and architecture decisions

Deliver production-quality, scalable, and maintainable systems

Testing & Quality Engineering

Write comprehensive tests (pytest, pytest-

asyncio, Jest)

Cover edge cases, failure scenarios, and

asynchronous workflows

Follow best practices for code quality,

observability, and maintainability