Dedicated AI Engineering Team
AI engineers who build production LLM applications, RAG systems and AI features.
- Understand requestIntent: refund · order #4821
- Retrieve policyRefund policy v3 · 2 sources cited
- Call toolsorders.lookup · payments.status
- 4Human approvalRefund above auto-approve limit
- 5Execute actionpayments.refund
- 6Respond & logCustomer reply · audit trail
Tool call
orders.lookup({
order_id: "4821"
}) → { status: "delivered",
amount: 42.00 }Approval required
Refund 42.00 to original payment method
Overview
A dedicated AI engineering team builds and operates AI capabilities: LLM applications, retrieval systems, copilots and AI features inside your products. The team brings evaluation discipline, cost control and security practices, and works with your data and product teams.
Ideal for
- AI product development
- AI features in SaaS
- Enterprise AI programmes
Typical composition
- 01AI Tech Lead
- 02AI / LLM Engineers
- 03Backend Engineer
- 04Data Engineer
- 05Evaluation Specialist
Composition is tailored to your roadmap; profiles are shared for your review before anyone starts.
Responsibilities
- AI feature development
- Retrieval and prompt engineering
- Evaluation and quality measurement
- Model gateway and cost control
Skills
Core technologies
How it works
Communication, delivery, security and scaling
Team composition
Teams are shaped to your roadmap, stack and domain, with profiles shared for your review before anyone starts.
Responsibilities
A written responsibilities matrix defines who owns what — priorities, architecture, delivery, quality and operations.
Communication
Teams work in your tools and rituals: stand-ups, planning, demos and retrospectives, with agreed time-zone overlap.
Delivery
Delivery managers track progress and risks, with regular demos and transparent reporting against agreed metrics.
Security
Least-privilege access, company-managed devices, secure credential handling and confidentiality agreements for every engineer.
Scaling
Teams scale up or down with notice periods agreed in the engagement, with knowledge transfer built into every change.
Engagement
Monthly engagements with clear terms, IP assigned to you and regular performance reviews with your leadership.
Engagement models
Choose how this team works with you
Individual Specialist
1 engineerA senior specialist embedded in your team to fill a specific skill gap.
Best for: Adding expertise such as smart contracts, AI or platform engineering to an existing team.
Dedicated Pod
2–4 peopleA small, self-organising unit focused on a feature area or workstream.
Best for: Owning a defined area of your product with minimal management overhead.
Dedicated Team
5–10 peopleA full cross-functional team working exclusively on your roadmap.
Best for: Building or evolving a product or platform over the long term.
Extended Team
FlexibleShivacha engineers integrated into your existing teams as additional capacity.
Best for: Accelerating an in-house roadmap without changing team structure.
Product Squad
4–8 peopleProduct, design and engineering together, accountable for product outcomes.
Best for: New products or product lines needing end-to-end ownership.
Managed Engineering
Scoped to outcomesShivacha owns delivery of defined outcomes with service levels and reporting.
Best for: Delegating a product, platform or operational workstream entirely.
Services
What this team delivers
AI Development
End-to-end AI development: from use-case selection and data preparation to models, applications and production operations.
Learn moreGenerative AI Development
Generative AI applications for text, documents, code and media — grounded in your data and engineered for production reliability.
Learn moreRAG Development
Retrieval-augmented generation systems that ground LLM answers in your documents with permissions, citations and measurable accuracy.
Learn moreAI Copilot Development
Embedded AI copilots that help users inside your product or internal tools — drafting, analysing, searching and taking actions.
Learn moreRelated teams
Often combined with
Machine Learning Team
ML engineers and data scientists for predictive models and MLOps.
Learn moreAI Agent Team
Specialists in agentic systems, tool integration and workflow automation.
Learn moreData Engineering Team
Engineers for pipelines, warehouses, streaming and blockchain data.
Learn moreFAQ
Frequently asked questions
How quickly can the team start?
Timing depends on roles and seniority. We share profiles for your review, and initial team members can often begin within a few weeks of agreeing scope.
Will the team work in our time zone?
We arrange meaningful overlap with your working hours and agree communication rituals up front. Teams work in your tools — your repositories, tracker and chat.
Who owns the code and IP?
You do. All work product is assigned to you under the engagement agreement.
Build your engineering team.
Tell us what your AI engineering team should own and we will propose a composition.