AI Architect

Inherent Technologies; Hoboken (NJ); On-site; USD 140,000 - 190,000; Full time

Job summary

Inherent Technologies seeks an AI Architect to lead onsite development in Hoboken, NJ. You will design multi-agent workflows, build robust data models, and align AI choices with business goals.

Responsibilities include monitoring model performance, optimizing inference costs, and ensuring latency targets are met. This role requires deep learning expertise, cloud experience, and strong programming skills, with a focus on scalable AI solutions.

Qualifications

• Mastery of deep learning frameworks (PyTorch or TensorFlow) and LLM/RAG pipelines.
• Experience with cloud services (AWS, GCP, Azure) and container orchestration (Docker, Kubernetes).
• Knowledge of data pipelines, ETL processes, vector databases, and data governance.
• Proficiency in Python and data engineering libraries; knowledge of Java is a plus.

Responsibilities

• Design multi-agent workflows using Lang Chain equivalents and related architecture patterns.
• Design data models for AI systems and ensure scalable data flow.
• Track model performance, manage inference costs, and monitor latency.

Skills

AI & ML mastery

Cloud & Infrastructure

Data Management

Programming: Python, Java

Tools

PyTorch

TensorFlow

AWS

GCP

Azure

Docker

Kubernetes

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