What are the responsibilities and job description for the ML Architect position at NextGenPros Inc?
Title: Machine Learning Architect
Location: San Jose, CA (Hybrid)
Long Term Contract On C2C
Job Summary
We are seeking an experienced Machine Learning Architect to design, develop, and oversee scalable AI/ML solutions that support business objectives. The ML Architect will lead the architecture of machine learning systems, define technical standards, collaborate with data scientists and engineers, and ensure production-ready deployment of AI models.
Key Responsibilities
- Design end-to-end machine learning and AI architectures for enterprise applications.
- Define standards, frameworks, and best practices for model development, deployment, monitoring, and governance.
- Collaborate with data scientists, data engineers, software engineers, and business stakeholders.
- Evaluate and select appropriate ML algorithms, tools, cloud services, and infrastructure.
- Architect MLOps pipelines for continuous integration, deployment, monitoring, and retraining.
- Ensure scalability, security, reliability, and performance of ML platforms.
- Guide model lifecycle management, including experimentation, validation, deployment, and maintenance.
- Lead technical reviews and provide architectural guidance to development teams.
- Stay current with advancements in AI, machine learning, deep learning, and generative AI technologies.
- Support regulatory compliance, data privacy, and AI governance requirements.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field.
- 8 years of experience in software engineering, data science, or machine learning.
- 3 years of experience designing enterprise-scale ML architectures.
- Strong knowledge of machine learning, deep learning, and statistical modeling techniques.
- Proficiency in Python and ML frameworks such as:
- TensorFlow
- PyTorch
- Scikit-learn
- Experience with cloud platforms:
- Amazon Web Services (AWS)
- Microsoft Azure
- Google Cloud Platform
- Experience with containerization and orchestration tools:
- Docker
- Kubernetes
- Strong understanding of MLOps, CI/CD, data engineering, and distributed computing.
Preferred Qualifications
- Experience with Generative AI and Large Language Models (LLMs).
- Knowledge of vector databases, retrieval systems, and AI agents.
- Experience with model governance, explainability, and responsible AI practices.
- Relevant cloud or AI certifications.