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AI Solutions Architect
Location
India
Posted
80 days ago
Salary
0
Seniority
Senior
Job Description
AI Solutions Architect
Weekday (YC W21)
• Design and oversee the development of scalable generative AI systems and enterprise-grade AI platforms. • Establish robust architectures that support model training, inference, monitoring, and lifecycle management in production environments. • Direct the selection, customization, and enhancement of state-of-the-art generative AI and large language models. • Develop and execute APIs, microservices, and integration frameworks to incorporate AI capabilities into enterprise applications. • Ensure that AI platforms meet stringent standards for performance, reliability, security, and scalability, while also adhering to data governance and privacy regulations. • Collaborate closely with product, engineering, and business teams to outline technical requirements and approaches to AI architecture. • Architect end-to-end pipelines for deploying and monitoring AI models, ensuring seamless integration with existing systems. • Guide architectural decisions for LLM applications, AI workflows, and distributed AI infrastructure. • Institute best practices for ethical AI development, including strategies to mitigate risks like model hallucinations, bias, and reliability challenges. • Provide technical mentorship and guidance to engineering teams, while contributing to the formulation of long-term technology strategies and the advancement of AI platforms.
Job Requirements
- More than 4 years of experience in roles related to software engineering or architecture, with significant involvement in AI/ML systems.
- Extensive knowledge of contemporary neural network architectures, such as Transformers, CNNs, and RNNs.
- Demonstrated ability in developing scalable and distributed architectures for applications driven by AI.
- Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Proficient in containerization and orchestration technologies, especially with Docker and Kubernetes.
- Strong understanding of microservices architecture, RESTful APIs, and the design of distributed systems.
- Familiarity with MLOps / LLMOps pipelines, covering aspects like model training, deployment, monitoring, and lifecycle management.
- Reasonable understanding of large-scale data systems and modern database technologies.
- Adept at transforming business requirements into scalable AI solution architectures.
- Excellent documentation skills for architectural designs, workflows, and technical decision-making processes.
- Ability to thrive in a startup or fast-paced environment, demonstrating a strong sense of ownership and leadership.
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