Data & AI
Python engineers, data engineers, data scientists, AI/ML engineers, and Gen AI and automation developers (including n8n) who build features on real data, not demos.
Why teams choose AppiaCS for data & ai
- Python, data, and ML engineers who ship production systems, not notebooks
- Gen AI developers building LLM features, RAG, and agents grounded in your real data
- AI and workflow automation (including n8n) that removes manual work between your existing tools
- Data pipelines and warehousing designed to be trusted, not just built
"AI" has become a catch-all term, so we're specific about what we actually staff: Python engineers building backend services and automation, data engineers who design pipelines and warehousing you can trust, data scientists who turn that data into decisions, and AI/ML engineers who take a model from a notebook into a production system that runs reliably under real load.
We also staff Gen AI developers specifically — engineers building LLM-powered features, internal copilots, and agentic workflows on top of foundation models, not just calling an API and calling it done. That includes prompt and context design, retrieval pipelines, evaluation, and the unglamorous work of making a generative feature reliable enough to ship to real users.
Alongside that, we build AI and workflow automation — including n8n — to connect the tools you already use (CRM, support desk, spreadsheets, internal systems) so data moves between them without someone copy-pasting it. Data and AI work is only as good as the pipeline underneath it, so we typically start by understanding what data you actually have, what shape it's in, and what decision or feature it needs to support, before writing a line of model code.
This is a fit if:
- You need a data pipeline or warehouse that the rest of the business can actually trust
- You want to add a generative AI feature (copilot, agent, search) and ship it reliably
- You're manually moving data between tools and want it automated (n8n or similar)
Data & AI FAQs
AI/ML engineers build and productionize predictive models (forecasting, classification, recommendations). Gen AI developers work specifically with LLMs — copilots, agents, retrieval-augmented generation — on top of foundation models rather than training models from scratch. We staff both, separately, because they're genuinely different skill sets.
Other disciplines
Ready to talk about data & ai?
Book a free scoping call — we'll follow up within one business day. We also offer flexible staffing models if you need capacity inside your own team.