Sr. Engineer – Agentic AI

Highbrow Technology Inc; United States; On-site; USD 150,000 - 180,000; Full time

Job summary

Highbrow Technology Inc. is seeking a senior-level engineer to advance Agentic AI initiatives in a fast-paced environment. You will architect and implement backend systems, collaborate on multi-agent workflows, and integrate AI models with robust observability and testing practices.

The role emphasizes Python, FastAPI/Flask, REST APIs, and modern cloud tooling (Azure). Experience with LangGraph/LangChain and vector databases is highly valued, with opportunities to influence architecture and

Qualifications

• 5–6+ years of experience in Core Application Engineering.
• 2+ years of hands-on experience in Agentic AI Engineering.
• Strong expertise in Agentic AI, Multi-Agent Systems, AI Workflows, Agent Orchestration, and MCP (Model Context Protocol).

Skills

Core Application Engineering

Agentic AI Engineering

Agentic AI

Multi-Agent Systems

AI Workflows

Agent Orchestration

MCP (Model Context Protocol)

Tools

LangGraph

LangChain

CrewAI

AutoGen

Python

FastAPI/Flask

REST APIs

Docker

Kubernetes

CI/CD

GitHub

Azure DevOps

Azure AI services

PostgreSQL

MongoDB

Redis

Vector Databases (ChromaDB, Pinecone, FAISS)

Job description

• 5–6+ years of experience in Core Application Engineering.
• 2+ years of hands‑on experience in Agentic AI Engineering.
• Strong expertise in Agentic AI, Multi‑Agent Systems, AI Workflows, Agent Orchestration, and MCP (Model Context Protocol).
• Strong experience with LangGraph and LangChain; CrewAI and AutoGen are good to have.
• Knowledge of Prompt Engineering, RAG, AI Observability, AI Testing & Evaluation, LLM Fine‑Tuning concepts, Tool Calling, and Function Calling.
• Strong backend development experience with Python, FastAPI/Flask, and REST APIs.
• Experience with Docker, Kubernetes, CI/CD, GitHub, and Azure DevOps.
• Experience with Azure AI services, including Document Intelligence, Azure Vision, and Cognitive Services.
• Experience with PostgreSQL, MongoDB, Redis, and Vector Databases such as ChromaDB, Pinecone, and FAISS.