We are seeking a highly skilled and experienced Generative AI (GenAI) Architect to join our team. This onsite role involves leading the design, development, and deployment of cutting-edge GenAI solutions tailored to business and client needs. The ideal candidate will bring deep expertise in generative AI models and frameworks, cloud infrastructure, and scalable AI system architecture.

Roles and Responsibilities

• Design and architect scalable, efficient, and secure Generative AI solutions based on business use cases.
• Lead end-to-end implementation of GenAI systems—from prototyping to deployment—ensuring performance, security, and reliability.
• Collaborate with engineers, data scientists, and product managers to integrate AI capabilities into existing systems and workflows.
• Evaluate and apply the latest GenAI research, tools, and technologies to solve real-world problems.
• Create and maintain detailed technical documentation including design specs, implementation guides, and best practices.
• Provide technical leadership, mentorship, and training to junior engineers and client teams.
• Optimize AI system performance, including tuning vector queries and database interactions.
• Ensure AI solutions comply with governance, security, and data privacy requirements.

Required Qualifications

• 5+ years of experience in AI/ML with a focus on Generative AI technologies.
• Proven success leading end-to-end GenAI project lifecycles—from idea to deployment.
• Strong programming skills in Python, with hands-on experience in GenAI frameworks (e.g., LangChain, Hugging Face Transformers, OpenAI, etc.).
• Experience deploying GenAI models in cloud environments (preferably AWS).
• Solid understanding of vector databases, embedding models, and vector query optimization.
• Proficient in SQL and familiar with structured/unstructured data handling.
• Experience with Natural Language Processing (NLP) techniques and models.
• Excellent communication skills—able to present technical solutions to both technical and non-technical audiences.
• Demonstrated leadership in mentoring or guiding engineering teams.

Preferred Qualifications

• Familiarity with DevOps practices and CI/CD pipelines for AI solution deployment.
• Experience with MLOps and AI model lifecycle management in AWS or other cloud ecosystems.
• Exposure to tools such as Docker, Kubernetes, Terraform, or CloudFormation.
• Prior experience working in Agile or cross-functional environments.
• AWS Certifications or relevant AI/ML certifications are a plus.

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