Dedicated Machine Learning Team
ML engineers and data scientists for predictive models and MLOps.
- 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
An ML team builds predictive models — forecasting, scoring, recommendation, vision — and the MLOps that keeps them healthy in production, from feature engineering through monitoring and retraining.
Ideal for
- Risk and fraud models
- Forecasting
- Recommendation systems
Typical composition
- 01ML Lead
- 02ML Engineers
- 03Data Scientist
- 04MLOps Engineer
Composition is tailored to your roadmap; profiles are shared for your review before anyone starts.
Responsibilities
- Model development
- Feature pipelines
- Model deployment
- Monitoring and retraining
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
Machine Learning Development
Custom machine learning models for prediction, scoring, forecasting and recommendation, deployed and monitored with MLOps.
Learn moreComputer Vision Development
Computer vision systems for inspection, detection, recognition and visual analytics — from cloud APIs to edge devices.
Learn moreNLP Development
Natural language processing for classification, entity extraction, sentiment, search and multilingual text understanding.
Learn moreAI Data Solutions
Data engineering for AI: pipelines, warehouses, feature stores, vector indexes and governance that make AI systems accurate.
Learn moreRelated teams
Often combined with
AI Engineering Team
AI engineers who build production LLM applications, RAG systems and AI features.
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 machine learning team should own and we will propose a composition.