AI Architect
Listed on 2026-07-01
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IT/Tech
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist, Cloud Computing: Infrastructure & Operations
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, 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 Tensor Flow, PyTorch, scikit-learn, and spaCy, with hands-on experience in integrating them into GenAI applications.
Preferred:
Hands on experience in GCP/Data Engineering/Dev Ops 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, 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.
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
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