Why US & UK Companies Are Choosing India AI/ML Teams in 2026
The cost-quality arbitrage on AI/ML talent has never been wider. A senior ML engineer in San Francisco, New York, or London costs $250–400 per hour fully loaded. An equally senior engineer in Bangalore, Pune, or Gandhinagar costs $80–150 per hour. That is not a junior-dev-shop discount — that is the same person who would have cleared a FAANG L5 loop, working on your problem, for a third of the rate. We are talking about engineers who have shipped production retrieval-augmented generation systems against 10M-document internal corpora, fine-tuned Llama 3 70B with QLoRA on H100 clusters, and stood up multi-agent workflows that move six-figure ARR for SaaS clients.
India's AI/ML talent depth is no longer a debate. The country graduates roughly 1.5 million engineers per year, the working population of software engineers crossed 4 million in 2025, and Indian institutions place in the global top 50 for AI research output (NeurIPS, ICML, ACL accepted papers). Three of the last five OpenAI senior research hires we know personally came out of IIT or IISc. The talent is here. What was missing until recently was the offshore delivery muscle to package that talent into something a US VP of Engineering or a UK Head of Data could actually buy — with a real DPA, real time-zone overlap, real IP transfer, and engineers who write English better than half the on-shore juniors they would otherwise interview. That is exactly the gap hjLabs.in fills.
Why hjLabs.in for US & UK Clients
Six concrete reasons offshore engagements with us look different from the typical India dev shop
4-hour real-time overlap with US east coast
We run a 4pm–midnight IST shift for US clients — that is 6:30am–2:30pm EST. You get morning standups, a full overlap window for pairing and code review, and async handoffs covering the rest. UK overlap is 4.5 hours (1pm–9pm IST = 8:30am–5pm BST). This is not a "we will get to it tomorrow" engagement.
Fluent technical English across the team
Every engineer we put on a US/UK account is comfortable on a Zoom call, writes RFCs, leads design reviews, and presents to your CTO. We do not staff anyone who would need a translator on a standup. This is the single biggest filter we apply during hiring — harder than the LeetCode bar.
GDPR, HIPAA, SOC 2 process compliance
We operate against the SOC 2 Type II framework (controls in place, audit underway), GDPR Article 28 DPAs are templated and ready to sign, HIPAA Business Associate Agreement available for healthcare clients, India DPDP Act 2023 and EU AI Act readiness on the engineering side. We say "framework" and "aligned" where we are not yet formally certified — we do not pretend.
Data residency: AWS Mumbai, Frankfurt, or your VPC
If your data cannot leave the EU, we run inside your AWS Frankfurt or your VPC. If you require US data residency, we deploy into your US-East-1. For healthcare clients with strict PHI handling, our default posture is engineers connect to your VPC over Tailscale or AWS Client VPN and no production data ever lands on a personal machine.
Trusted by enterprise teams across 12 countries
Anonymised, our client base spans a US fintech (KYC/AML LLM workflows), a UK healthtech (clinical-notes RAG), an EU manufacturer (computer-vision defect detection on production lines), a US e-commerce platform (multi-agent product-recommendation system), an Australian regtech, and a Canadian legaltech. References available under NDA once we have signed mutual.
Senior engineers only — no junior-dev-shop pricing tricks
The classic offshore vendor sells you a senior architect on the pitch and staffs the project with juniors. We do not. Our floor for client-facing engineers is 6 years of production ML experience — that means people who have actually shipped, debugged a 3am production incident, and read papers since BERT. The engineer in your standup is the engineer in your repo.
Services for Offshore Clients
Five core service lines — each anchored by a senior ML engineer who has done the work in production
RAG systems
Internal-corpus retrieval-augmented generation at 10M+ document scale — hybrid BM25 + dense retrieval, reranking, citation-grounded outputs, eval harnesses, and the production gotchas (chunk-boundary leakage, embedding-drift monitoring, multi-tenant isolation) we have hit across 18+ RAG engagements. Typical first deployment: 8–12 weeks. See RAG service detail →
LLM fine-tuning
LoRA, QLoRA, and full fine-tunes on Llama 3, Mistral, Phi-3, Gemma, and domain models. We do dataset curation, hyperparameter sweeps on H100 clusters, eval design against your golden set, and the ship-to-vLLM-or-TGI deployment that turns weights into a real production endpoint. 40–90% accuracy lift over zero-shot prompting on domain tasks. See LLM fine-tuning detail →
Agentic AI
Multi-agent workflows, tool use, function calling, and Anthropic Model Context Protocol (MCP) servers. We build agents that actually do work — pull from your CRM, write to your data warehouse, file Jira tickets, and produce audit logs for compliance. We have shipped agents into SaaS dashboards, internal ops tools, and customer support stacks. See agentic AI detail →
Computer vision
Defect detection on factory lines, document AI for KYC/invoicing/clinical-form extraction, vision-language models for product search, and on-device inference (TensorRT, CoreML, OpenVINO). YOLO v8/v9, SAM, Florence-2, and custom CNNs — whatever the latency/accuracy/cost frontier needs. See computer vision detail →
MLOps + production
Drift detection, model registries, eval pipelines, observability (Arize, Langfuse, Datadog ML), feature stores, batch/streaming inference, A/B routing, and the boring-but-critical stuff that makes the difference between a model demo and a model in production. Cuts post-launch surprise rate by an order of magnitude. See MLOps service detail →
Need something else?
Reinforcement learning, recommender systems, forecasting, NLP information extraction, speech (Whisper fine-tunes, TTS), and ML infra (Ray, Kubeflow, vLLM clusters) are all on the menu. If you have a problem and are not sure which service line it falls under, the free 30-minute scoping call is exactly for that. Book scoping call →
Engagement Models for US & UK Clients
Three shapes — pick the one that matches your buying authority and project clarity
Fixed-scope project
$25k–$150k · 8–16 weeks · defined deliverables
You know the problem, you want a price, you want a date. We do the scoping, write a fixed-price SOW with milestones, and ship. Best for: first RAG deployment, first fine-tune, first computer-vision prototype-to-production, first agentic workflow. Includes 30/40/30 milestone billing and a 4-week post-launch warranty.
Dedicated senior engineer most popular
$14k–$22k/month · 40h/week · full-time embedded
A named senior ML engineer joins your Slack, your Linear, your GitHub, your daily standup. Reports to your engineering lead. Works on your tickets the same way an in-house hire would. You manage them, we handle payroll, taxes, equipment, and back-up coverage for sick days and PTO. Minimum 3-month commitment. Try-then-hire conversion available after month 6.
Fractional ML team
$30k–$80k/month · 3–5 engineers · ongoing partnership
For companies that need an entire ML function but are not ready to hire a US-based 4-person team at $1.5M/year fully loaded. We bring a tech lead + 2–4 senior engineers + part-time MLOps + part-time product/data PM. Acts as your offshore ML org. Common shape: replaces a $1.2M on-shore team with $600k offshore. Minimum 6-month commitment.
How Offshore Engagement Actually Works
Four phases from first email to production handoff — no consulting fluff
Week 0: Scoping call
Free, 60 minutes, Hemang on the call. We confirm technical fit, talk through your data, sign mutual NDA, sketch a timeline. No sales engineers, no pre-canned slides — an engineering conversation.
Cost: $0Week 1–2: Discovery
Data audit (volume, quality, labels, PII), success-metric definition (what "good" looks like in numbers), infra requirements (cloud, on-prem, hybrid), DPA + SOW signed. Output: a 10-page technical brief you can show your CTO.
Cost: $5k–$12kWeek 3–N: Build
Daily standup during US east-coast overlap window (4pm–midnight IST = 6:30am–2:30pm EST). Slack channel with your team. Linear or Jira board (your choice). Weekly demo. Bi-weekly stakeholder review. Code into your GitHub from day one.
Cost: per SOWOngoing: Handoff
Production runbooks, monitoring dashboards (drift, latency, cost), on-call rotation if you want it, and a 4-week paid knowledge transfer to your in-house team. We do not lock you in — the goal is your team owns the system 6 months after we hand it over.
Cost: 4 weeks bundled
Compliance + Data Security
Compliance is the part most US/UK buyers want to talk about first, so we cover it head-on. We are not a fly-by-night offshore vendor that signs whatever you put in front of us and hopes nothing breaks — we have a written security and compliance posture you can audit.
- DPDP Act 2023 (India) + EU AI Act readiness — India's Digital Personal Data Protection Act took effect in late 2024 and we built our internal data-handling controls to match. For EU clients we map AI Act risk classifications (limited, high-risk, prohibited) onto your use case during discovery and write conformity assessments into the SOW where required.
- HIPAA process compliance for healthcare clients — we sign a Business Associate Agreement (BAA), engineers complete annual HIPAA training, all PHI handling is documented, and we operate against the HIPAA Security Rule's administrative, physical, and technical safeguards. We do not claim HIPAA certification because HIPAA does not certify entities — we provide BAA-backed process compliance.
- SOC 2 Type II framework — controls implemented across the five Trust Service Criteria (security, availability, processing integrity, confidentiality, privacy). External audit underway. We will say "SOC 2 Type II certified" the day the auditor signs — not before.
- On-prem / VPC option — for high-sensitivity engagements, engineers connect to your AWS VPC, Azure VNet, or GCP project over Tailscale or AWS Client VPN. No production data leaves your environment. Personal devices are excluded via posture checks (managed device, disk encryption, MDM).
- Background-checked engineers + signed NDA per project — every client-facing engineer goes through a third-party background verification (criminal, address, education, prior employer) before being staffed to a project. Per-project NDA on top of the master MSA so confidentiality survives engineer rotation.
Pricing — Transparent Rates
USD-first because our buyers are in USD. Equivalent on-shore rates shown for benchmark.
| Role | Hourly $ (USD) | Monthly $ (USD) | Equivalent on-shore rate |
|---|---|---|---|
| Senior ML Engineer | $80–$120 | $14k–$22k | $300–$450/hr |
| Staff ML Engineer | $120–$150 | $22k–$28k | $400–$550/hr |
| ML Architect | $150–$180 | $28k–$32k | $500–$650/hr |
| Junior ML Engineer | $40–$60 | $7k–$11k | $150–$200/hr |
| Project Manager | $60–$90 | $11k–$16k | $200–$300/hr |
Above are floor rates. Specialised verticals (defense, healthcare, regulated finance) +20–30%. Multi-month engagements get 10–15% discount. INR pricing available on request (typical Senior ML Engineer floor: ₹6,500–₹10,000/hr).
FAQs — What US & UK Buyers Actually Ask
The eight questions that come up in every scoping call
How do you handle time zone differences with US east/west clients?
Our engineers shift to a 4pm–midnight IST schedule for US clients, giving you a 4-hour real-time overlap with US east coast (6:30am–2:30pm EST) and a 1.5-hour overlap with US west coast (4am–12pm PST). For UK clients we run a 1pm–9pm IST shift, giving a 4.5-hour real-time overlap with London. Daily standups happen during the overlap window, and async hand-offs cover the rest via Slack, Linear, and Loom video updates.
What is the typical project ramp-up time?
For dedicated-engineer engagements we usually have a named senior engineer in your Slack within 5–7 business days of contract signing. For fixed-scope projects, the discovery phase starts within 3 business days and the build phase begins after the 2-week scoping is signed off. Fractional ML team engagements take 10–14 business days because we hand-pick the team for your stack and domain.
Can we have a dedicated engineer who joins our Slack daily?
Yes. Our dedicated-engineer model is built around exactly that workflow. The engineer joins your Slack, your Linear or Jira, your GitHub, and your daily standup. They report to your engineering lead, follow your code review process, and treat your tickets the same way an in-house hire would. The only difference is they invoice through hjLabs.in.
Do you sign NDAs and DPAs?
Yes, both. We sign a mutual NDA before the scoping call and a Data Processing Agreement (DPA) before any production data is shared. Our default DPA is GDPR Article 28 compliant and includes standard contractual clauses (SCCs) for cross-border data transfer between India and the EU/UK/US. For healthcare clients we also sign a Business Associate Agreement (BAA) for HIPAA process compliance.
How do you handle IP — do we own the code?
You own 100% of the code, models, weights, fine-tunes, datasets, and documentation we produce for you. Our master services agreement transfers all IP to the client on payment, with no carve-outs except for general-purpose internal tooling and open-source contributions that pre-date your project. We also write a clean assignment-of-IP clause into every dedicated-engineer contract.
What is the kickoff cost?
Zero. The 60-minute scoping call is free, the NDA is free, and the technical fit assessment is free. You only pay once you sign an SOW or dedicated-engineer agreement. For fixed-scope projects we typically bill 30% on signing, 40% on milestone 1, and 30% on final delivery. For dedicated engineers and fractional teams we invoice monthly in arrears, net-15.
Can we hire engineers full-time after a project (try-then-hire)?
Yes — about a third of our dedicated-engineer engagements end with the client offering the engineer a full-time role. We support that with a transparent buy-out clause: after 6 months of dedicated engagement, you can convert the engineer to your full-time payroll for a one-time conversion fee. Before 6 months, the standard recruiting fee applies. We do not block engineers from accepting offers.
Do you work with US healthcare, EU healthtech, or regulated finance?
Yes. About 40% of our engagements are in regulated verticals — US healthcare (HIPAA BAA in place), EU healthtech (GDPR + EU AI Act high-risk classification), and US/UK regulated finance (SOC 2 Type II framework, ISO 27001 process alignment). We follow least-privilege data access, signed audit logs, and on-prem or VPC deployment when the regulator requires data residency.
Book a Free 30-Minute Scoping Call
Hemang Joshi on the call. Engineering conversation, no slide deck. If we are not a fit we will say so and point you somewhere better.
Author of this page: Hemang Joshi, Founder & Principal AI/ML Engineer at hjLabs.in · LinkedIn · GitHub