Engineering & AI Recruitment
Engineering and AI hiring
for teams where technical
quality matters.
Talhive is an engineering recruitment firm built for technology companies that cannot mis-hire on technical roles. The Engineering & AI practice covers the full technical stack, from founding engineers and AI/ML specialists to engineering leadership and platform teams. Delivered through retained executive search, India team builds, and embedded RPO.
The strongest engineering and AI candidates are employed, selective, and not on job boards. They are inside platform teams, AI infrastructure groups, and product engineering functions at companies they recognise. Reaching them requires a sourcing thesis built around what they have shipped, not what they call themselves.
Six sub-practice areas.
Each with distinct market logic.
Engineering Leadership
CTO, VP Engineering, Head of Engineering, Staff and Principal Engineers. Leadership roles where the hiring decision sets the architectural and cultural ceiling for the team that follows.
AI, ML & Data Science
Production AI engineers, ML research scientists, applied AI architects. The production LLM deployment population is much smaller than the AI label suggests, and mostly employed inside platform teams.
Backend, Platform & Infrastructure
Backend engineers, platform engineers, distributed systems, API and integration specialists. The deepest talent pool in India, but scarcity compounds fast at senior levels and niche stacks.
Mobile & Frontend
Senior Android and iOS engineers, React Native, frontend engineers building at consumer scale. Product-first mobile engineers, those who own quality and UX decisions, require a different sourcing lens than typical mobile contractors.
Data Engineering & Analytics
Data engineers, analytics engineers, data platform leads. The intersection of engineering rigour and business context is rare, these profiles do not typically surface through inbound recruiting or generic job postings.
DevOps, SRE & Cloud
Site reliability engineers, DevOps leads, cloud architects, security engineers. High demand and a smaller active candidate pool means passive outreach is almost always necessary at senior levels.
Four structural constraints that
break standard recruiting approaches.
The strongest candidates are not looking
Senior engineers with production AI experience, mobile engineers who have owned consumer apps at scale, platform engineers who have built for millions of users, these profiles are employed, compensated well, and have no reason to respond to a job posting. Passive outreach with a calibrated narrative is the only way in.
Production experience is not the same as research experience
In AI/ML especially, the gap between a research profile and a production deployment profile is material. Most previous search failures in this space come from treating them as the same pool. Talhive builds the sourcing thesis around what the candidate has shipped, not what they call themselves.
Compensation expectations have moved
India engineering compensation at senior and specialist levels has shifted significantly. Anchoring the brief to internal bands built two years ago creates offer-stage friction at best and a failed search at worst. Talhive tests the compensation narrative against live market data before the first approach is made.
Unknown brands face a harder initial conversation
A senior engineer with options weighs employer brand, tech stack, team quality, and equity before salary. A global company unknown in the India market must build a candidate-facing narrative before the first outreach, not after the first decline. Talhive builds that positioning into the mandate design.
How Talhive evaluates
engineering candidates.
An engineering recruitment firm that does not assess depth is just a CV forwarder. Every shortlisted engineering candidate is assessed across a structured competency framework before the client sees them. The output is a written intelligence brief, not a CV summary.
See the full assessment methodologyVP Engineering, Series B SaaS
Three previous agencies. Four offer-stage dropoffs. The problem was motivation, not sourcing. We surfaced equity concerns before any offer was made.
Principal AI Engineers ×3, US-backed AI product company
Six months of inbound recruiting and one prior agency had produced no hires. We rebuilt the sourcing thesis around what candidates had shipped, not what they called themselves.
Which model fits
your engineering hire?
CTO, VP Engineering, Staff Engineers, AI/ML Leads
When the candidate pool is narrow, motivation matters, and a wrong hire has real organisational consequences.
Executive SearchFounding engineering teams, GCC builds, first India hires
Leadership-first sequences for US, European, and APAC companies building India engineering capability from zero.
India Team BuildScale engineering hiring, 5+ roles open simultaneously
Embedded recruiter with engineering-specific assessment, pipeline ownership, and weekly reporting as the team scales.
RPO / Embedded HiringThe engineering or AI mandate
you are working on.
Bring the brief. Talhive will tell you what the market looks like, what the sourcing thesis should be, and whether the current approach is likely to produce the right outcome.
Direct answers.
Anything not covered here is answered in a call.
Discuss a mandate →