Senior AI Architect

Join SADA as a Senior Artificial Intelligence Architect

Your Mission 

As a Senior Artificial Intelligence Architect at SADA, you will work in a fast-paced, highly-skilled team developing state-of-the-art enterprise solutions using a wide variety of artificial intelligence technologies. You will work directly with customers, engineers, sales teams and partners to deliver high quality professional service engagements to SADA’s customers. SADA is a premier partner of Google Cloud Platform (GCP), and as such, our implementations focus on enabling customers to best utilize GCP or to demonstrate its capabilities. 

You will be responsible for building cloud-native artificial intelligence solutions using Google’s Vertex AI platform and all related technologies, such as Gemini large language models, Agent Builder, LangChain, Vector Search and more. You will also be responsible for implementing domain-specific solutions such as Search for Retail, Media and Healthcare, and Contact Center AI Platform. This is a hands-on role that will include both architecting and implementing AI solutions.

Artificial intelligence is an extremely dynamic and quick-changing discipline. Therefore, your approach for learning new technology will be as important as any prior knowledge. We are looking for a “professional tinkerer” with a passion for continuous learning and a knack for problem solving. Someone that learns fast and pivots even faster. That always says “I’ll figure it out” instead of “I don’t know.”

Your day-to-day will consist of the following activities:

• Implementing AI–based solutions for SADA clients across all project stages, from discovery and architecture, to development, testing and launch
• Providing subject matter expertise and assistance to SADA clients and colleagues on artificial intelligence topics

• Collaboration with client stakeholders managing the project, including other engineers and Director-level client resources

Expectations

Customer-Facing - Daily interactions with customers, including standups or sales meetings; at other times, weekly/bi-weekly as for sprint planning, reviews/retrospectives, or demos. These interactions will be with roles ranging from engineers to key executive stakeholders.

Travel - Willingness and ability to travel to customer sites, conferences, and other related events when needed (minimal travel is expected)

Onboarding/Training -The first several weeks of onboarding are dedicated to learning and will encompass learning materials/assignments and compliance training, as well as meetings with relevant individuals. Details of the timeline are shared closer to the start date.

Job Requirements

Required Credentials:

• One of the following (within 90 days of hire):
• Google Professional Machine Learning Certification
• Google Professional Cloud Architect Certification
• Google Professional Data Engineer Certification

Required Qualifications:

• 4+ years of relevant work experience in technical consulting, software development or cloud engineering
• 1+ years of relevant work experience designing and implementing Generative AI solutions, including use and tuning of large language models, RAG, LangChain, vector embeddings, and multimodal use cases
• Strong consultative skills, including the ability to translate complex AI concepts into actionable business strategies and solutions
• Ability to communicate complex technical concepts to a broad range of internal and external stakeholders
• Familiarity with frontend web prototyping technologies such as Streamlit 
• Expertise in development using Python, and familiarity with other programming languages such as JavaScript, Go or Java
• Prior experience designing, building, and maintaining high-quality, developer-friendly server-side code, such as webhooks, cloud functions, and data pipelines
• Prior experience with full stack development, including setting up cloud infrastructure, database services and application lifecycle management
• Ability to architect, scope, and prioritize work based on feedback from the customer or internal teams.
• Experience using Google Cloud Platform, in particular serverless (Cloud Functions), containers (Cloud Run), and platforms (App Engine)

Useful Qualifications:

• Experience working with RDBMS, including SQL databases like MySQL, PostgreSQL or BigQuery
• Exposure to containers and container orchestration, such as Docker

• Experience working with Authentication/Authorization
• Experience working with Data Science/ML platforms like Jupyter

• Experience in writing automated tests, including unit, integration, and acceptance tests
• Experience working with CI/CD pipelines and version control