AI Architect

Apptad Inc; Hoboken (NJ); On-site; USD 150,000 - 200,000; Full time

Job summary

Apptad Inc in Hoboken, NJ is seeking an AI Architect to design advanced multi-agent AI systems, focusing on scalable deployments and cost-aware inference.

You will define data models, implement retrieval-augmented generation pipelines, and guide engineering teams through trade-offs while aligning AI solutions with business goals.

Candidates should master DL frameworks, LLMs, and cloud platforms, and be proficient in Python and Java to deliver robust, compliant AI architectures.

Qualifications

• Mastery of deep learning frameworks like PyTorch or TensorFlow.
• Experience with large language models (LLMs) and retrieval-augmented generation (RAG) pipelines.
• Proficiency with cloud services (AWS, Google Cloud, Azure) and container orchestration (Docker, Kubernetes).
• Strong knowledge of data pipelines, ETL processes, vector databases, and data governance.

Responsibilities

• Design multi-agent workflows using frameworks like LangChain or Lang Graph.
• Design data models and scalable architectures for AI solutions.
• Track model performance, manage inference costs, and monitor latency.
• Ensure security and compliance, prevent data leaks, and mitigate model hallucinations.

Skills

Deep learning

LLMs

RAG pipelines

PyTorch

AWS

Tools

LangChain

LangGraph

Python

Java

Docker

Kubernetes

Job description

Role : AI Architect

Location: Location: Hoboken, NJ Onsite

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