Generative AI Architect

Job Title: Generative AI Architect Position Type: Contract Location: Jersey City, NJ (Hybrid) Position Overview We are seeking a highly skilled and experienced Generative AI (GenAI) Onsite Architect to join our team. The ideal candidate should have a deep understanding of AI technologies, particularly in the area of generative AI models, and will be responsible for designing and implementing AI solutions. Key Responsibilities: • Develop and design scalable and efficient GenAI solutions tailored to our requirements. • Lead the implementation of GenAI systems, ensuring they are robust, secure, and performant. • Work closely with cross-functional teams, including engineers and product managers to integrate AI solutions into existing systems. • Stay up-to-date with the latest advancements in AI and apply this knowledge to drive innovation within the company. • Create comprehensive documentation for AI solutions, including design specifications, implementation guides, and user manuals. • Provide training and mentorship to junior team members and clients on GenAI technologies and best practices. Qualifications: • Minimum of 5 years of experience in AI/ML, with a focus on generative models. • Proven track record of successfully implementing AI projects in real-world settings. Experience in managing the end-to-end lifecycle of AI projects, from conception to deployment. • Proficiency in programming languages such as Python and other AI frameworks. • Strong analytical and problem-solving skills, with the ability to think critically and creatively. • Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders. • Proven leadership abilities, with experience managing and mentoring technical teams. • Ability to work in a fast-paced, dynamic environment and adapt to changing project requirements. • Experience with cloud platforms such as AWS. • Familiarity with natural language processing (NLP) Preferred Qualifications: Familiarity with DevOps practices and CI/CD pipelines for automated software deployment and delivery