From: aidotengineer
Building a dedicated team for a GenAI platform is a critical component for success, especially given the unique challenges and opportunities presented by Generative AI systems [00:00:59].
Why a Dedicated GenAI Team is Critical [00:00:59]
GenAI systems differ significantly from traditional AI systems. In traditional AI, there’s a clear separation between model optimization and model serving phases, allowing AI engineers and product engineers to operate in different tech stacks without needing to work on the same codebase [00:10:04]. However, in GenAI, this distinction blurs, meaning everyone is an engineer who can optimize overall system performance [00:10:24]. This creates new challenges for tooling and best practices within a company [00:10:41].
GenAI and agent systems are considered “compound AI systems,” tackling AI tasks using multiple interacting components, such as multiple calls to models, retrievers, or external tools [00:10:49]. A dedicated GenAI platform team helps bridge the skill gap between AI engineers and product engineers [00:11:10].
Ideal Candidate Qualities [00:11:55]
When building and recruiting AI teams for GenAI, an ideal candidate possesses a mix of technical and soft skills:
- Strong Software Engineer: Capable of building infrastructure integrations [00:12:01].
- Developer PM Skills: Good at designing interfaces [00:12:07].
- AI and Data Science Background: Understanding of the latest techniques [00:12:14].
- Hands-on Learner: Able to learn from the latest techniques while being practical [00:12:19].
Finding a single “unicorn” engineer with all these qualities can be very challenging [00:12:25].
Hiring Principles [00:12:38]
Given the difficulty in finding ideal candidates, companies often make trade-offs in hiring. Key principles include:
- Prioritize Software Engineering Skills: Strong software engineering skills are generally prioritized over AI expertise [00:12:47].
- Hire for Potential: Focus on a candidate’s potential rather than just experience or degrees, as the field evolves rapidly and experience can quickly become outdated [00:13:03].
- Critical Thinking: Emphasize critical thinking, as solutions built today may be outdated within a year or even less than six months. Teams must constantly evaluate new open-source packages, engage with vendors, and proactively deprecate their own solutions [00:14:06].
Building a Diversified Team [00:13:15]
To overcome the challenge of finding a single engineer with all desired qualifications, a recommended approach is to hire a diversified team [00:13:21]. This can include:
- Full-stack software engineers [00:13:27]
- Data scientists [00:13:31]
- AI engineers [00:13:34]
- Data engineers [00:13:37]
- Fresh graduates from top research universities [00:13:41]
- Individuals with startup backgrounds [00:13:44]
By combining these diverse skills within projects, strong engineers will naturally pick up new skills and evolve into the ideal candidates over time [00:13:50]. This strategy contributes to building successful AI projects with small teams and fostering effective AI implementation.
Tech Stack Considerations [00:14:33]
When choosing a tech stack for a GenAI team, Python is strongly recommended due to its prevalence in research and open-source communities [00:14:37]. It has proven to be scalable in practice [00:14:50].
Key Components for GenAI Teams [00:15:01]
Teams building GenAI platforms should focus on:
- Prompt Source of Truth: Establish a robust system for version controlling prompts, similar to how traditional model parameters are managed. This is crucial for operational stability and preventing accidental production errors [00:15:03].
- Memory Management: Build a system for managing conversational and experiential memory, which allows for injecting rich data into the agent experience and extracting tacit knowledge from interactions [00:15:26]. This is vital for building AI agents.
- Skill Uplifting: In the agent era, a key new component is uplifting APIs into “skills” that agents can easily call. This requires surrounding tooling and infrastructure [00:15:42]. This relates to the role of engineering teams in AI agent production.
- Observability: Because agents are autonomous and can make decisions on which APIs or LLMs to call, predicting their behavior is difficult. Investing in observability tools (e.g., in-house solutions on top of OpenTelemetry) to track low-level telemetry data allows for replaying agent calls and guiding future optimization [00:06:50]. This is a key aspect of building resilient AI workflows.
Scaling and Adoption Strategies [00:16:04]
To ensure successful adoption and scaling of the GenAI platform:
- Solve Immediate Needs First: Instead of trying to build a full-fledged platform from the outset, focus on solving immediate needs. Start with simpler components (e.g., a Python library for orchestration) and allow the platform to grow organically [00:16:07].
- Focus on Infrastructure and Scalability: Leverage existing robust infrastructure where possible, such as using messaging infrastructure as a memory layer for cost efficiency and scalability [00:16:29]. This contributes to leadership and organizational strategies for AI integration.
- Prioritize Developer Experience: The ultimate goal of a GenAI platform is to enhance developer productivity. Design the platform to align with existing developer workflows to ease adoption and increase success [00:16:46]. This addresses a common issue in implementing Gen AI projects.