AI Engineers
AI engineers for product teams that need real product work, not demos
Silicon Development supports AI work when the job is to ship a feature inside a real product. The focus is on engineers who can handle prompt design, retrieval systems, inference infrastructure, and application integration with the same production discipline the rest of the product requires.
What this role usually owns
These are the kinds of problems this role should be able to take responsibility for inside an operating product team.
Integrating LLMs into the product with prompt management, orchestration, and guardrails
Building RAG pipelines and vector search infrastructure that perform at production scale
Shipping AI-powered features inside an existing application rather than as a standalone demo
Setting up model serving, inference optimization, and monitoring for production AI workloads
When teams usually need this role
- The team has been asked to add AI features but nobody on staff has shipped LLM integrations or RAG systems in production
- You need an engineer who treats AI work like product engineering, with tests, monitoring, and deployment discipline included
- You want AI capability inside the current application, not a separate proof of concept that never reaches users
What we screen for
- Ability to integrate AI systems into real applications with latency, security, and quality constraints
- Judgment around retrieval, prompting, observability, and evaluation rather than hype-driven tooling decisions
- Communication that keeps AI work legible to product and engineering leadership instead of turning it into a black box
Related case studies
A few examples of the environments and outcomes this role supports.
Consumer Tech / Data Platform
Pragma.AI
Serving as the primary engineering team for Pragma.AI
Read case study →BioPharma
BioPharma AI Platform
Helping a biopharma platform move from MVP to production
Read case study →LegalTech / FinTech
Enterprise Litigation Platform
Adding embedded engineering capacity to a litigation platform
Read case study →AI work only pays off when it survives contact with production
The right AI engineer should make the feature more usable, observable, and maintainable, not turn it into a side project.