TechDigital Group
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
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 ,
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 .
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 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 (e.g., FastAPI)
3 #J-18808-Ljbffr
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
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 ,
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 .
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 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 (e.g., FastAPI)
3 #J-18808-Ljbffr