Artificial Intelligence Solutions Architect
il y a 4 semaines
About the Role
Cisco is seeking a highly skilled Solutions Engineer - Artificial Intelligence (AI) to drive the adoption of our AI solutions across various industries. As an SE (AI), you will identify potential clients, understand their specific needs, and provide tailored AI solutions that enhance their business operations.
Key Responsibilities
- Drive the adoption of AI solutions across various industries
- Identify potential clients and understand their specific needs
- Provide tailored AI solutions that enhance business operations
- Collaborate with technical teams, account teams, and executives to showcase AI solutions and promote their tactical and technical responsibilities
- Travel as needed to meet with clients and attend industry events
Requirements
- 6+ years of technical presales experience selling Compute, Storage, and Network solutions
- Proficiency in Python, MySQL, Github, Git, GO, ETL, OLAP, RDBMS, Scribus, AWS, Azure, Google, IBM Cloud, or other platforms and programming languages
- Experience with databases (Oracle, PostgreSQL, MySQL, MongoDB, Cassandra, Redis, Snowflake, BigQuery)
- Working practical experience with AI/ML frameworks (scikit-learn, TensorFlow, PyTorch, Hugging Face)
- Excellent presentation skills – ability to deliver engaging workshops to both technical and non-technical audiences on AI topics
- Ability to provide detailed and consumable documentation of standard methodologies for deployment around application acceleration, automation/management efficiencies, enterprise edge, and AI/ML solutions
Preferred Qualifications
- Bachelor's Degree in Computer Science, Computer Engineering, Electrical Engineering, or related field. Advanced degree is a plus
- AI experience with Nvidia, IBM, Microsoft, Dell, NetApp, HPE, and/or other AI vendors
- In-depth understanding of language models, including but not limited to GPT-3, BERT, or similar architectures
- Familiarity with containerization technologies (e.g., Docker or equivalent experience) and orchestration tools (e.g., Kubernetes) for scalable and efficient model deployment
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