Location: Remote | Hours: Full-time | Compensation: $90K–$110K + Growth & Equity Potential | Timing: ASAP (immediate availability required for this role)

About Incepta

We're building production AI systems that solve real problems for insurance brokers; not demos or pilots, but systems that handle complex workflows and replace manual processes at scale. Our focus on context optimization, advanced prompting techniques, and sophisticated tool orchestration has uncovered specific market opportunities that others are missing.

What we're looking for

We need engineers who've already proven they can ship. If you've built side projects with real users, won hackathons, or architected AI agents from scratch, we want to talk. We're specifically seeking people who know how to move fast, learn aggressively, and take ownership of outcomes.

You’ll own projects end-to-end, from architecture through deployment. You’ll be architecting agentic systems for unique insurance workflows, iterating directly with customers, and driving solutions from concept to deployment. The work will prepare you for technical leadership because that's where we expect high performers to end up.

Why Incepta

Our approach is deliberate: we've identified specific automation opportunities in insurance that require sophisticated AI implementation. You'll work alongside cracked engineers who've built scalable systems before, learn an industry with massive automation potential, and help us execute on a vision that prioritizes innovation over incremental improvements.

We're building toward permanent roles and equity for people who prove they can contribute at this level. If you want to work on challenging technical problems while building something that actually matters, let's talk.

Role & responsibilities

Design & ship AI agents end-to-end; from spec to production deployment
Orchestrate LLM pipelines (GPT-4o, o1-preview, Claude 3.5 Sonnet, Gemini 2.0) with LangChain/LangGraph and vector search
Integrate APIs (OpenAI, Anthropic, custom services) with proper auth, error handling, and logging
Build lightweight UIs with React + Tailwind enabling users to trigger and monitor agents
Deploy & scale on serverless platforms; optimize for speed, accuracy, and cost
Implement function calling & tool use for agent-environment interactions via Model Context Protocol (MCP)
Build computer vision pipelines for document processing (PDF parsing, OCR, structured data extraction)
Monitor & iterate with performance tracking and weekly improvements

Required skills

(Applicants must meet ALL criteria to be considered)

Education: Bachelor’s, Master’s, or PhD in Computer Science, AI, or related technical field
Experience: Minimum 2+ years of hands-on experience building and shipping AI systems
Programming: Proficient in Python or JavaScript (Node.js)
Prompt Engineering: Strong written and verbal communication skills; able to craft prompts that get models to follow complex instructions and handle open-ended tasks
LLM APIs: Hands-on experience with OpenAI APIs (GPT-4 family, o1-preview, Function-Calling, Vision, Realtime API)
Multimodal AI: Experience with additional LLM providers (Anthropic Claude, Google Gemini)
RAG Implementation: Hands-on experience with retrieval-augmented generation, vector search, and document processing pipelines
Function Calling & Tool Use: Built agents that interact with external APIs, databases, and services
Serverless Deployment: Deploy code on AWS Lambda, GCP Functions, Azure Functions, or Firebase
Data Systems: Experience with SQL/NoSQL databases and vector databases (Pinecone, ChromaDB, FAISS)
Full-Stack Integration: Connect backend AI logic with frontend interfaces
Agent Frameworks: Experience with LangChain, LangGraph, LlamaIndex, or similar orchestration tools

Bonus skills

(Nice to have, not required)

Computer Vision Integration: Combining vision models with LLMs for document/image analysis
Model Context Protocol (MCP): Structured agent-environment interaction protocols
Real-time AI Systems: Streaming, WebSocket, or low-latency AI applications
Containerization: Docker and basic Kubernetes
Frontend Frameworks: React/Vue for dashboards and chat interfaces
SaaS Scaling: Taking prototypes to production-ready systems
AI Safety: Responsible AI principles and data privacy practices

Tech stack / skills

Python · Node.js · TypeScript · REST/GraphQL APIs · LLM APIs (GPT-4o, o1-preview, Claude 3.5, Gemini) · Function Calling & Tool Use · RAG Implementation · LangChain/LangGraph/LlamaIndex · Model Context Protocol (MCP) · Computer Vision/Document Processing · AWS Lambda · GCP Cloud Functions · Azure Functions · React + Vite · SQL & NoSQL · Vector Databases (Pinecone/ChromaDB/FAISS) · Multi-agent Orchestration · Docker · CI/CD · Prompt Engineering

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