AI Architect

Inherent Technologies; Hoboken (NJ); On-site; USD 150,000 - 230,000; Full time

Job summary

Inherent Technologies in Hoboken, NJ is seeking an AI Architect to design multi-agent workflows, data models, and cost-aware inference strategies. This onsite role focuses on aligning AI tech choices with business goals while guiding cross-functional teams through architecture decisions.

You will work with PyTorch, TensorFlow, LLMs, and RAG pipelines, along with AWS, Google Cloud, Azure, Docker, and Kubernetes to build scalable ML systems with privacy and governance in mind.

Qualifications

• Expertise in ML frameworks, LLMs, and RAG pipelines.
• Cloud platforms and container orchestration experience.
• Knowledge of data pipelines, ETL, and vector databases.

Responsibilities

• Design multi-agent workflows using Lang Chain or Lang Graph and architectural patterns.
• Design data models.
• Track model performance, manage inference costs and latency.
• Ensure privacy, prevent data leaks, and mitigate model hallucinations.
• Align AI choices with business goals and budgets.
• Lead engineering teams through design trade-offs and execution.

Tools

PyTorch

TensorFlow

AWS

Google Cloud

Azure

Docker

Kubernetes

LangChain

Lang Graph

ETL

Vector databases

Data governance

Python

Java

Job description

Position: AI Architect

Location: Hoboken, NJ *Onsite *

Duration: 1 Years

Skill Set :

AI & Machine Learning: Mastery of deep learning frameworks like PyTorch or TensorFlow, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines.

Cloud & Infrastructure: Experience with cloud services AWS Saga Maker, Google Cloud AI, Microsoft Azure AI Docker, Kubernetes

Data Management: Knowledge of data pipelines, ETL processes, vector databases, and data governance.

Programming: Proficiency in core languages such as Python & Data Engineering Libraries and Java.

Job Description :

Designing multi-agent workflows using frameworks like Lang Chain or Lang Graph and architectural design patterns

Designing data models

Tracking model performance, managing inference costs, and handling latency tracking.

Security & Compliance: Guarding privacy, stopping data leaks, and mitigating model hallucinations.

Aligning technical AI choices with real business goals and financial budgets.

Guiding engineering teams through design trade-offs and project execution