Hourly
Weekly invoicing, minimum 40 hours per month, weekly demos. You pay only for the hours we log against your project board. Best for an LLM integration, a RAG pipeline tune-up, or an AI feature spike to de-risk a roadmap bet.
LLM integration · RAG · AI agents · MLOps
Hire AI developers. Senior LLM and agent engineers.
Senior AI engineers — LLM integration, retrieval-augmented generation, agent orchestration, evals and guardrails, MLOps — into your team in 14 calendar days. We ship AI features to real users, not demos: grounded retrieval, tool-using agents, and the evaluation and cost controls that keep them reliable in production.
eCorpIT places senior AI engineers — LLM integration, RAG, agents, MLOps — into your team in 14 calendar days. Rates run $44/hr mid-level to $68/hr architect, against US senior AI rates of $120–$220/hr. NDA before any technical conversation; evals and guardrails from Sprint 1. Manu reviews every AI engagement personally.
- 14 calendar days to first demo
- $56 senior rate /hr (vs $120–220 US)
- Evals and guardrails from Sprint 1
- 5.0 Google rating · 55 reviewers
- NDA-first · within 4 hours
- MSA + DPA before code
- Senior-only delivery
- Weekly demos, weekly invoicing
- Founder review at every milestone
Why eCorpIT
Why hire AI developers through eCorpIT.
Production AI is its own discipline. A working demo is the easy 20%; the hard 80% is keeping an LLM feature accurate, grounded, fast and affordable once real users hit it — retrieval that does not hallucinate, agents that recover from tool failures, evals that catch regressions before they ship, and a cost model that survives scale.
Manu Shukla reviews every AI engagement at architecture and milestone stages. The engineer whose CV you interviewed is the engineer who ships.
US seniors $90–$160/hr
UK seniors £50–£95/hr
eCorpIT $44–$48/hr (senior)
A 60–70% vs US specialists saving at the same seniority, on a dedicated pod with an MSA and DPA signed before code is written. Industry rate benchmarks.
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LLM integration that ships
OpenAI, Anthropic Claude, Google Gemini, Llama and on-prem models — behind a provider-agnostic layer so you can switch or fall back. Streaming, function/tool calling, structured output and token-cost budgeting built in, not bolted on.
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RAG that stays grounded
Retrieval-augmented generation with proper chunking, hybrid (vector + keyword) search, re-ranking and citation. Vector stores — pgvector, Pinecone, Weaviate, Qdrant — chosen for your scale, with answer-grounding checks to fight hallucination.
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Agents & orchestration
Tool-using agents with retries, guardrails and human-in-the-loop checkpoints. LangGraph, the OpenAI Agents SDK or a custom orchestrator — built to recover from failure, not just to demo a happy path.
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Evals & guardrails
Offline and online evaluation harnesses, regression suites, prompt-injection and PII guardrails, and tracing (LangSmith, Langfuse, OpenTelemetry) so quality is measured, not hoped for.
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MLOps & deployment
Model and prompt versioning, A/B routing, latency and cost monitoring, caching and batching. Inference on your cloud or a managed endpoint, with rollback when an eval regresses.
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On-device & private AI
On-device inference (Core ML, TensorFlow Lite, ONNX) for latency and privacy, plus private/VPC deployments for regulated data that cannot leave your perimeter.
Transparent pricing
AI rate card (USD, hourly).
AI work carries a modest premium over the mobile rate card — the talent pool is scarcer and the failure modes subtler. AI engagements are typically staffed at the Senior or Lead tier; Junior engineers work productively under senior pairing. All-in, weekly invoicing, net 14.
| Tier | Experience | AI / LLM Eng | ML / Data | MLOps |
|---|---|---|---|---|
| Junior | 1–2 years | $32/hr | $30/hr | $34/hr |
| Mid-level | 3–5 years | $44/hr | $42/hr | $46/hr |
| Senior | 6+ years | $56/hr | $54/hr | $58/hr |
| Lead / Architect | 8+ years | $68/hr | $66/hr | $70/hr |
For a US-bought AI build at one Senior AI engineer over six months, that is about $54K eCorpIT versus $128K at the US senior midpoint — same shipped capability, same production rigour.
How you work with us
Four engagement models.
You pick the one that fits how you actually want to work. We do not push everyone into the same shape.
Monthly
160 hours per month, dedicated. 10% discount on the hourly rate. The engineer attends your standups, sits in your Slack, follows your sprint cadence. Pause with 30 days’ written notice. Best for active builds.
Quarterly
3-month commit, 15% discount on hourly. Includes a shared designer (40 hours) and a shared QA engineer (40 hours) at no additional cost. The pod model — a real team behind one engineer.
Project
$15K (AI feature spike) MVP starting (a premium over the $8K mobile start because evals, guardrails and grounding are real engineering, not a prompt). We own scope, milestones and acceptance criteria. Weekly demos, weekly invoicing against milestones. Best when the spec is well-defined and you want predictability.
All four models include the NDA signed before any technical conversation, an MSA with India and EU/UK-aligned clauses, a DPA aligned with GDPR and India’s DPDP Act, weekly invoicing, and a single named delivery lead for the whole engagement. Evals and guardrails from Sprint 1.
Real seniors on the bench
Sample engineer profiles (anonymised).
We send full anonymised CVs on request and arrange interviews within 5 business days of NDA.
Senior AI / LLM Engineer
8 years
Python + TypeScript, OpenAI and Anthropic in production, RAG with pgvector and Pinecone, hybrid search and re-ranking, LangGraph agents, LangSmith evals. Shipped a US legal-tech RAG assistant (grounded citations, 12k docs/tenant), a UK support-automation agent (tool-using, human-in-the-loop) and an Indian fintech document-extraction pipeline.
Available: Monthly or quarterly
Senior ML / Applied AI Engineer
7 years
PyTorch, fine-tuning and LoRA, embeddings, recommendation and ranking, on-device inference (Core ML, TFLite). Shipped a US consumer personalisation engine, a UK health-triage model (on-device, privacy-first) and a global vision-based QA pipeline. Specialism: making models small, fast and cheap enough to ship.
Available: Hourly, monthly or quarterly
AI Lead / Architect
12 years, 5 in LLM systems
Provider-agnostic LLM architecture, eval-driven development, prompt-injection and PII guardrails, cost and latency budgeting, MLOps (versioning, A/B routing, tracing). Led architecture for three production GenAI builds, including a regulated-data RAG system deployed in a customer VPC.
Available: Monthly, quarterly or fixed-price
Full anonymised CVs and arranged interviews follow the 14-day onboarding (Days 2–5). Additional AI references on request under NDA — RAG assistants, support-automation agents, document-extraction pipelines and on-device ML.
The promise
The 14-day onboarding, day by day.
This is a calendar-day commitment, not a "best efforts" promise. If we miss any of these dates, the first month of the engagement is on the house.
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NDA signed
Within 4 hours of your first inbound message. Sign it; the technical conversation starts immediately.
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Discovery call
60 minutes. Manu Shukla joins. We map the problem, target users, your existing stack and constraints. We map the use case, the data sources for retrieval, the model and provider choices, the eval criteria that define "good enough", and the cost and latency budget. You leave with a one-page strategy doc by end of day.
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Shortlist CVs
3 anonymised CVs of senior AI engineers matched to your build, each with 3 shipped AI features and a 30-minute video introduction.
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Interviews
You interview 2–3 of the shortlisted engineers. We do not block on “first available.” You pick.
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Team selection & plan
Engineer (or pod) finalised, engagement model agreed, draft project plan with milestones shared.
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MSA + DPA signed
Master Services Agreement and Data Processing Addendum (GDPR + DPDP aligned) signed both sides.
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Environment setup
Repository, Slack/Teams, Jira/Linear/Asana, model-provider sandbox keys, a vector store, an eval harness and a tracing dashboard.
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Kick-off
Founder-led kick-off call with Manu, the delivery lead, the assigned engineer(s) and your team. Sprint 0 deliverables aligned.
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Sprint 1 planning + technical design
Technical design document for the first deliverable. Sprint 1 backlog locked.
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Sprint 1 in build
Daily standups in your timezone. Slack-first communication.
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First demo
Working build of the first AI feature with retrieval grounded, guardrails on and an eval baseline in place. Retro. Sprint 2 plan agreed.
No surprises
Included in every AI engagement.
The line items that show up as “extras” with other vendors are baked into our rates.
- Architecture review with a data-flow and grounding analysis by a Lead before Sprint 1.
- Code review by a second senior engineer on every PR, with eval and guardrail review built in.
- An evaluation harness (offline + online) and a regression suite for every AI feature.
- Prompt-injection, jailbreak and PII guardrails, with a redaction layer for sensitive data.
- Token-cost and latency budgeting, with caching and batching to keep unit economics sane.
- Tracing and observability (LangSmith / Langfuse / OpenTelemetry) wired from day one.
- Model and prompt versioning, with rollback when an eval regresses.
- Accessibility audit (WCAG 2.2 AA) for any user-facing AI surface before submission.
- Weekly demos, Friday status notes, founder review at milestones.
- ISO 27001:2022-aligned source-code and data handling. 30 days post-launch support.
Not included unless quoted separately: LLM and embedding API usage, GPU/inference compute, vector-store and tracing-tool licences, and any third-party model fine-tuning costs are billed at cost or quoted separately.
Procurement-ready
The credentials a US or UK procurement team checks.
We have them all on file and will share them under NDA.
- CMMI Level 5 appraised
- ISO/IEC 27001:2022 — Information security
- ISO 9001:2015 — Quality management
- ISO/IEC 20000-1:2018 — Service management
- ISO 45001:2018 — Occupational H&S
- GDPR-aligned data handling
- DPDP Act (India) compliant
- DPIIT recognised startup
- MSME registered
- D-U-N-S® verified · #854367803
AI-governance-aware delivery: we ship evals, guardrails, audit logging and data-handling controls that map to emerging AI assurance expectations (NIST AI RMF, EU AI Act risk tiers). GDPR / UK GDPR + DPA 2018 aligned, with private/VPC deployment available for regulated data.
5.0 from 55 reviewers on Google, on the canonical AI Mobile App Development page. Founder LinkedIn and verified company profiles are linked from the footer.
Free project estimate · 24 hours
Tell us about your project. Get a free estimate in 24 hours.
Within 24 working hours, you receive a one-page PDF: recommended scope, suggested tech approach, indicative pricing range, and a delivery timeline. Reviewed by a senior architect from the eCorpIT team. No sales pressure, no follow-up unless you ask for one.
- One-page strategy doc + indicative range
- NDA available on request
- Reviewed personally by a senior architect
- No sales pressure. No follow-up unless you ask.
Request received
Got it — we'll be in touch within 24 hours.
A senior architect will review your brief and reply by email with a one-page strategy doc, indicative pricing, and a realistic timeline.
- 1 We review your brief and map it to the right practice + team.
- 2 You get a one-page estimate with scope, pricing range, and timeline.
- 3 If it's a fit, we book a 30-min discovery call — no obligation.
Reference — · We'll reply from contact@ecorpit.com.
Answers, up front
How much does it cost to hire AI developers?
Through eCorpIT, senior AI engineers bill $32–$68/hour by seniority — roughly 60–70% below US cost for the same experience — across LLM integration, RAG, agents and MLOps. You hire a vetted senior engineer (plus optional ML and data support) on hourly, monthly or pod terms, onboarded within 14 days.
What should I look for when hiring an AI developer?
Look for shipped production AI, not notebooks: LLM integration with evals and guardrails, retrieval (RAG) that stays grounded, agent orchestration, and MLOps for versioning and monitoring. eCorpIT matches senior engineers who have shipped AI features to real users, with evaluation and cost controls built in from day one.
FAQ
Questions, answered.
What US and UK teams ask before hiring AI developers with us.
No matches. Try a different keyword or contact us directly.
What kinds of AI features do you build?
LLM integration (chat, copilots, structured extraction), retrieval-augmented generation, tool-using agents, recommendation and ranking, and on-device inference. We ship features to real users with evals and guardrails, not proof-of-concept notebooks.
Which AI models and providers do you work with?
OpenAI, Anthropic Claude, Google Gemini, Meta Llama and on-prem/open models, behind a provider-agnostic layer so you can switch or fall back without a rewrite.
How do you stop an LLM from hallucinating?
Grounded retrieval with citation, answer-grounding checks, structured output where possible, and an eval suite that flags ungrounded or low-confidence answers before they ship. Guardrails block prompt injection and PII leakage.
Can you deploy AI on our own cloud or VPC?
Yes — inference on your AWS/Azure/GCP account or a private VPC endpoint, with open models where data cannot leave your perimeter. Required by regulated-data customers.
What are your AI rates vs a US AI agency?
eCorpIT senior AI rate is $54–$58/hour. US AI-specialist agencies bill $120–$220/hour at the same seniority. Arbitrage is 60–70% on like-for-like work.
How do you control AI running costs?
Token-cost budgeting, caching, batching, model routing (cheap model first, escalate on need) and latency monitoring — built into the architecture, with cost dashboards from day one.
Do you build AI into mobile apps specifically?
Yes — AI in mobile is our core. On-device inference for latency and privacy, plus server-side LLM features behind a clean mobile API. See our AI mobile app development service.
Who actually writes the code?
The engineer whose CV you interviewed. No bait-and-switch, no junior backfill on production AI workloads.
Keep reading
Related hiring guides & services.
Ready to start a ai engagement?
Two ways in. Either way, Manu joins the call personally for every new engagement.
NDA back within 4 hours · discovery call booked within 24 hours.