AI Systems Engineer (Backend + GenAI Infrastructure)

We are looking for a strong Systems Engineer with a passion for AI to build the backbone of our agentic platform. You wont just be identifying insights or prompting modelsyou will be architecting the cloud infrastructure, state management systems, and asynchronous runtimes that allow complex AI agents to plan, code, and execute reliability at scale.

This is a high-impact role for a builder who understands that a great AI agent is only as good as the system architecture running it.

What Youll Work On

Core Systems & Cloud Architecture (70%)

Build Scalable Backend Services: Design and implement robust APIs (FastAPI) that handle multi-turn conversations, manage complex state, and persist execution artifacts (plans, code, logs) to a permanent database layer.

Asynchronous Agent Orchestration: Architect the communication layer between front-end interfaces and backend agents, implementing async patterns (WebSockets, SSE, Queues) to handle long-running tasks.

Cloud & Security: Deploy secure, horizontally scalable infrastructure on platforms like Vercel, Google Vertex AI, or AWS. Handle authentication, rate limiting, and secure execution sandboxes.

Knowledge Management Support: Build the "Knowledge Studio" backendAPIs and storage schemas that allow users to manage prompts, documentation, and domain concepts dynamically.

AI Engineering & ML Ops (30%)

Model Orchestration: Deploy and manage Small Language Models (SLMs) on the cloud for specialized implementation subtasks (e.g., ambiguity resolution), optimizing for latency and cost.

RAG & Context Engineering: Implement retrieval pipelines that dynamically fetch the right context for the agent, ensuring high relevance and low hallucination.

Fine-tuning & Evaluation: Manage pipelines for fine-tuning models on specific domains (coding, tool selection) and running automated evaluations of agent performance.

What Were Looking For

We are looking for a strong Systems Engineer with a passion for AI to build the backbone of our agentic platform. You wont just be identifying insights or prompting modelsyou will be architecting the cloud infrastructure, state management systems, and asynchronous runtimes that allow complex AI agents to plan, code, and execute reliability at scale.

This is a high-impact role for a builder who understands that a great AI agent is only as good as the system architecture running it.

What Youll Work On

Core Systems & Cloud Architecture (70%)

Build Scalable Backend Services: Design and implement robust APIs (FastAPI) that handle multi-turn conversations, manage complex state, and persist execution artifacts (plans, code, logs) to a permanent database layer.

Asynchronous Agent Orchestration: Architect the communication layer between front-end interfaces and backend agents, implementing async patterns (WebSockets, SSE, Queues) to handle long-running tasks.

Cloud & Security: Deploy secure, horizontally scalable infrastructure on platforms like Vercel, Google Vertex AI, or AWS. Handle authentication, rate limiting, and secure execution sandboxes.

Knowledge Management Support: Build the "Knowledge Studio" backendAPIs and storage schemas that allow users to manage prompts, documentation, and domain concepts dynamically.

AI Engineering & ML Ops (30%)

Model Orchestration: Deploy and manage Small Language Models (SLMs) on the cloud for specialized implementation subtasks (e.g., ambiguity resolution), optimizing for latency and cost.

RAG & Context Engineering: Implement retrieval pipelines that dynamically fetch the right context for the agent, ensuring high relevance and low hallucination.

Fine-tuning & Evaluation: Manage pipelines for fine-tuning models on specific domains (coding, tool selection) and running automated evaluations of agent performance.

What Were Looking For

Technical Skills

System Design: Strong proficiency in Python (FastAPI/Django) and experience designing schema for complex applications (PostgreSQL, NoSQL).

Cloud Native: Experience deploying and scaling on modern cloud platforms (GCP Vertex, AWS, Vercel) and using containerization (Docker/Kubernetes).

AI Integration: Practical experience integrating LLMs/SLMs via APIs and orchestrating workflows (LangChain, LlamaIndex, or custom orchestration).

Async Patterns: Understanding of how to handle long-running jobs (Celery, Redis, Queues) and real-time frontend updates.

Soft Skills & Mindset

Engineering First: You treat AI agents as software systems that need to be tested, debugged, and versioned.

Versatility: You are comfortable jumping from optimizing a SQL query to finetuning a Llama-3 model.

Problem Structuring: You can take a high-level goal like "handle ambiguity" and break it down into a technical workflow involving specific model calls and UI interaction states.

Bonus Points For

Experience with Vector Databases (Pinecone, Weaver, pgvector) at scale.

Experience building Agentic Workflows (planning, tool use, reflection loops).

Familiarity with finetuning techniques (LoRA, PEFT) for SLMs.

Background in frontend integration (React/Next.js) to understand the full user lifecycle.

Who Will Thrive in This Role

Youll be a great fit if you:

Are a software engineer first who loves AI, rather than a data scientist trying to learn engineering.

Want to build the engine, not just the fuel.

Enjoy the challenge of making nondeterministic LLMs behave reliably in a production system.

System Design: Strong proficiency in Python (FastAPI/Django) and experience designing schema for complex applications (PostgreSQL, NoSQL).

Cloud Native: Experience deploying and scaling on modern cloud platforms (GCP Vertex, AWS, Vercel) and using containerization (Docker/Kubernetes).

AI Integration: Practical experience integrating LLMs/SLMs via APIs and orchestrating workflows (LangChain, LlamaIndex, or custom orchestration).

Async Patterns: Understanding of how to handle long-running jobs (Celery, Redis, Queues) and real-time frontend updates.

Soft Skills & Mindset

Engineering First: You treat AI agents as software systems that need to be tested, debugged, and versioned.

Versatility: You are comfortable jumping from optimizing a SQL query to finetuning a Llama-3 model.

Problem Structuring: You can take a high-level goal like "handle ambiguity" and break it down into a technical workflow involving specific model calls and UI interaction states.

Bonus Points For

Experience with Vector Databases (Pinecone, Weaver, pgvector) at scale.

Experience building Agentic Workflows (planning, tool use, reflection loops).

Familiarity with finetuning techniques (LoRA, PEFT) for SLMs.

Background in frontend integration (React/Next.js) to understand the full user lifecycle.

Who Will Thrive in This Role

Youll be a great fit if you:

Are a software engineer first who loves AI, rather than a data scientist trying to learn engineering.

Want to build the engine, not just the fuel.

Enjoy the challenge of making nondeterministic LLMs behave reliably in a production system.