AI Architect, New Jersey

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 , JavaScript , Java , or other backend languages. Experience with API development (RESTful APIs, GraphQL ).

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

Preferred:

Hands on experience in GCP/Data Engineering/DevOps

JD

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 , DevOps , 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 .

 

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 engineering
5

 
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, NJ
Agentic AI implementation
3.5

 
Writing SQL 
4

 
API framework ( eg.FastAPI )
3