The forward-deployed AI engineer

Did you say you were a data engineer? Or was it an ML expert? Or data scientist? MLOps expert? A domain expert? Sorry, AI business analyst? Think about a new type of role that is gaining prominence in the industry: A forward-deployed AI engineer. This week, I am listing out the various aspects of the role.

Customer-embedded=

The individual is integrated into the customer’s work environment to eliminate any miscommunication and the risk of being lost in translation. The role is best performed on-site (at the customer’s office). In such a scenario, it becomes easy for the stakeholders to reach out to the engineer whenever necessary.

End-to-end AI models and platforms 

The key is to focus on the end-to-end-ness of the solution. LLMs, agents, and ML systems must be meaningfully stitched into the business flow. The solution must be easy to use to ensure adoption. One cannot say they have provided the customer with an ML model and then leave it to the business team to figure out how to use it. This is a frequent misunderstanding of accountability.

Real customer environment

We are not talking about a simulated environment having production-like data. Neither are we talking about building a prototype that works on one-tenth of the size of the dataset. How often have we remarked that this demo was meant to test the solution approach, and that we will scale it later?

Ownership of implementation and outcome

We must shift our value proposition from the solution implementation to its outcome. A successfully deployed, brilliant AI technical solution widely adopted by stakeholders, but not improving business as intended, is just another solution destined for the dustbin. The forward-deployed AI engineer takes accountability for the outcome.

Business processes, workflows, constraints, and problems

The engineer must skillfully understand the working steps, identify what hinders smooth operation, and determine business problems that need to be addressed. This forms the business side of the role’s demand.

Workable AI use cases

This involves the technical side of the role’s demand. We have frequently seen business analysts fall short of the business acumen needed for the subject. This is very likely because it is impossible to know as much as a businessperson in the customer’s organisation who has been doing the job for 20 years.

AI and ML knowledge

The engineer must be capable of designing and building prompts, agent workflows, data pipelines, and integrations with internal IT systems. This is true for both the traditional ML and large language models.

Security posture, compliance, and reliability

The engineer must be able to evaluate the solution’s security posture and reliability. They must ensure it complies with the land’s regulatory policies and procedures.

Monitoring and fixing bugs

The individual must keep monitoring the solution and fix bugs when encountered in the production environment.

Quality metrics

The engineer must identify quality metrics, retraining frequencies, and business KPIs. This will help determine the initiative’s success or failure, though success need not be a binary metric.

Feedback

The role must incorporate field feedback and continually improve the solution over time. One could collect the feedback and implement the improvement manually. However, the best approach is to automate the feedback and improvement loop, allowing for minimal human oversight.

The forward-deployed AI engineering role sounds like an all-in-one expectation in AI. While having the role on paper is one thing, ensuring an individual does justice to the role is another. It will be a combination of engineering, product, and consulting roles. It is software engineering with full-stack development expertise. It is knowledge of traditional ML, Generative AI, MLOps, and the golden bridge between technical teams and business stakeholders. 

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