Dedicated AI Engineering Team · India

Dedicated AI Engineers, Embedded in Your Team

AI specialists who join your sprints, work to your cadence, and ship production AI - not just prototypes.

Most engineering teams know what AI use cases they want to build. The gap is AI engineering capability - model integration, fine-tuning, deployment, and operational monitoring. Vijay Global's AI Remote Teams fill that gap without the time and cost of building it internally.

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Is This You?

Signs this service is what you need:

⚠ Your product or engineering team has AI use cases but no in-house AI engineers

⚠ You have pilots that need production expertise to move forward

⚠ You are running sprints and need AI specialists embedded - not consulted externally

⚠ Hiring a full internal AI team is too slow and expensive for your current stage

⚠ Your team needs AI capability for a defined period - not a permanent headcount addition

What We Deliver

Model Selection & Integration

Matching the right model to each use case - LLM, fine-tuned, or RAG-based - and integrating it into your existing stack.

Prompt Engineering & Evaluation

Systematic testing for accuracy, reliability, and edge case handling before any output is used in production.

Fine-Tuning & Domain Adaptation

Improving model performance on your specific data, vocabulary, and domain context.

Production Deployment

Packaging, monitoring, alerting, and operational runbooks so the system runs reliably after initial deployment.

Sprint Participation

Planning, standups, and retrospectives as a full member of your team - not an external resource dropping in occasionally.

How We Deliver

Onboarding (Weeks 1–2)

Team meets your stakeholders. Codebase and AI assets reviewed. Delivery priorities agreed. Tools and communication setup confirmed.

Active Delivery (Sprints)

Engineers participate in your sprint ceremonies. AI workload picked up from your backlog. Working, tested outputs delivered per sprint.

Monthly Review

Engagement reviewed monthly - scope, team size, and focus areas adjusted based on what is needed next.

Handover

Full documentation, architecture records, and knowledge transfer delivered at the end of each engagement phase.

Outcomes You Can Expect

AI features moving from backlog to production - not stuck at prototype stage

Model accuracy and reliability validated before reaching operational users

Engineering team upskilled through daily collaboration with AI specialists

Flexible team size - scale up or down based on sprint demands

Full audit trails and governance documentation maintained throughout

Related Services

AI-Centric Bespoke → RAG (Knowledge-Based) → Copilot →

Ready to Add AI Engineering Capability?

Share your backlog - we'll show you how a dedicated AI team fits into your sprints.

Discuss Your AI Team → Back to Gen AI Services