//
Demand for AI engineers in Western markets has driven local salaries to unsustainable levels. DelhiStack's offshore AI developers in India bring deep expertise in LLM orchestration, RAG architectures, and ML pipeline engineering - at 60% of comparable US rates.
We are not a freelancer marketplace. We are a premium offshore engineering agency with proven processes.
Pre-vetted engineers ready to join your Slack and GitHub within 72 hours of agreement.
Mandatory 4–5 hour daily overlap with your EST/PST or GMT team for real-time collaboration.
You own every line of code from day one. Strict NDAs signed before any discussion begins.
Daily standups, weekly demos, and full Jira/GitHub visibility. No surprises, ever.
Every developer has 3+ years of production experience. We do not staff junior engineers on client projects.
Scale your team up or down on a monthly basis - no long-term contracts or termination penalties.
The scarce skill in AI work is not prompt writing. It is building a system around a model that behaves predictably: retrieval that returns relevant context, evaluation that catches regressions, cost controls that stop a runaway loop, and fallbacks when the provider degrades.
We screen for people who have shipped an AI feature to real users and dealt with what follows — hallucinations reported by customers, latency that made a feature unusable, an invoice nobody forecast. Those experiences shape engineering judgement in ways that tutorials do not.
A candidate who cannot describe how they evaluated whether their system got better after a change is not ready to own production AI work, regardless of how fluent they sound about models.
Teams building retrieval-augmented systems usually focus on the model and treat retrieval as solved by embedding documents into a vector database. In practice retrieval quality determines output quality almost entirely, and naive chunking produces confidently wrong answers.
The work that matters is unglamorous: sensible chunking that respects document structure, hybrid search combining semantic and keyword matching, reranking, and metadata filtering so the system searches the right subset. Each of these moves answer quality more than switching models does.
We build an evaluation set before building the pipeline. Without a fixed set of questions and expected answers, 'is it better now' is an opinion, and teams end up tuning by vibes across weeks of work.
AI features have an unusual cost profile: spend scales with usage in a way most software does not. We instrument token consumption per feature from the start, set hard limits, and cache aggressively where responses are reusable. Discovering the economics from an invoice is an avoidable mistake.
Latency shapes what is possible. A multi-step agent chain that takes twenty seconds is fine in a background job and unusable in a chat interface. We design around the interaction the feature actually needs rather than assembling the most capable pipeline available.
On provider risk, we favour keeping the model layer behind an interface so switching providers is a configuration change rather than a rewrite. Model capability and pricing move quickly enough that locking your architecture to one vendor's API shape is a real liability.
Tell us your idea. We'll turn it into a world-class digital product. Free consultation, no commitment.