AI Architect

Mandatory

12+ years of hands-on technical experience in software engineering, with at least 3+ years in a leadership role managing cross-functional teams, including GenAI
, machine learning
, and cloud infrastructure
.

Hands-on Experience in designing and developing large-scale systems
, including GenAI
, API architectures
, data systems
, ML pipelines
, and cloud-native applications
.

Programming Languages
:
Proficiency in Python
, Java Script ,
Java
, or other backend languages.

Experience with
API development (RESTful APIs,
GraphQL
).

Machine Learning & AI
:
Extensive experience in building and deploying ML models using Tensor Flow
, Py Torch ,
scikit-learn
, and spaCy
, with hands-on experience in integrating them into GenAI applications.

Preferred:

Hands-on experience in GCP/Data Engineering/Dev Ops

Job Description

The AI architect will play a pivotal role in architecting
, leading
, and actively contributing to the development of GenAI applications,
machine learning models
, data engineering pipelines, and cloud-native infrastructure
. This hands-on leadership position requires extensive technical expertise and experience in managing a diverse, cross-functional team of engineers spanning GenAI App Development,
Data Science
, Machine Learning
, Full Stack
, Dev Ops
, Cloud Infrastructure
, and API development
.

Be responsible for architecting complex systems
, making critical decisions, and leading teams to deliver high-quality, scalable solutions while remaining directly involved in coding
, technical design
, and problem-solving
.

Skills Skill Required  Proficiency - On a scale of 1-5 (5 being the highest)
AI Architect Design and implement scalable architectures for Generative AI-based applications addressing challenges such as latency, fine-tuning, model monitoring, and cost optimization

Job Location /Client Location (with City & State)
Strong background in data science, machine learning, and software engineering5

Hands-on expertise in managing LLMs (e.g., OpenAI, Hugging Face, or custom models).4

Solid understanding of cloud platforms (e.g., AWS, Azure, GCP) and MLOps best practices.
4

Raritan, NJAgentic AI implementation
3.5

Writing SQL4

API framework (e.g., FastAPI)3 #J-18808-Ljbffr