Principle AI Engineer

Principal AI Engineer

Magnasoft
· Bengaluru (Hybrid)

Hands -on technical leadership · Scaling our AI/ML engineering
org

Why this role exists

Magnasoft is twenty years into building one of the
world's deepest geospatial data assets — and is now turning that asset into
AI -powered software products. We have the data, the domain, and the customer
relationships — and our first product, MTerra — our proprietary AI/ML cloud
platform for plat map vectorization and cadastral correction in land
administration — is in beta, with early deployments validated and commercial
traction building. What we're building next is the engineering organization.
That turns this from a services company into an operational AI platform
company.

Today, that AI/ML engineering work runs through a single
person: our VP & Head of Technology. This role exists to change that. As Principal
AI Engineer, you become the most senior engineer in the AI org and the
technical owner of how we build — the architecture, the delivery, and the
engineering craft of the team. This is a player -coach seat: you set the
technical direction and you stay in the code. You are not inheriting a
mature platform to maintain — you are scaling it from a strong foundation.

If you want to be one slice of a large, settled ML org,
this isn't it. If you've drifted away from the keyboard and want a pure
management seat, this isn't it either. If you want to define — and personally
build — the technical spine of a company at its inflection point, read on.

What you'll own

Architecture & technical direction. You own the architecture of the ML systems behind our
product portfolio — a shared computer -vision / OCR / human -in -the -loop engine
applied across multiple verticals (cadastral digitization today; fiber/telecom
and LiDAR/point -cloud next). Your most important architectural mandate: design
the path from today's periodically -retrained static models toward continuous,
online, and ultimately agentic model updates — systems that improve from a
live production feedback signal with the right safety gates, not a human
blessing on every release.

Hands -on engineering. You
write production code. You're in the hardest parts of the codebase, you set the
bar by example, and you multiply the team's output with modern AI -assisted /
Agentic coding workflows (e.g., Claude Code) rather than treating them as a
novelty. The team should get faster because you're on it — both through your
architecture and through how you actually build.

Technical delivery across the portfolio. You own the engineering bar and the delivery of the AI/ML
work across our active product lines. You make the build -vs -buy calls, set
standards for how models ship, and are accountable for the systems being
reliable enough to charge customers for.

The data flywheel. Our
moat is twenty years of labeled delivery data and a feedback loop that turns
every project into a training signal. You'll partner closely with the Senior
Applied ML Engineer (who owns model -quality methodology and the eval harness)
to make that loop real: data and label versioning, a trustworthy automated
evaluation gate, and the retraining cadence that lets models improve release
over release.

Technical leadership & mentorship. You are the technical authority the AI/ML engineers work
under day to day — architecture, design, code review, and mentoring. You raise
the team's ceiling and make it able to run without a single point of failure.
People -management (performance, 1:1s, career) sits with the VP & Head of
Technology, so your energy goes to the engineering, not the org chart.

Building the AI team. You
help build the team you'll lead. As we scale the AI/ML org, you own the
technical side of hiring — designing how we interview AI/ML engineers, running
technical interviews, setting the bar for who clears it, and growing a senior
technical bench so the team is never one person deep. The final hire decision
and offers sit with the VP & Head of Technology; getting the technical
judgment right is yours.

Near -term, ramping down: technical stabilization of the
legacy delivery program. For the first
phase, you'll own the technical stabilization and clean handover of our
largest existing delivery program to a maintenance footing — the retraining,
tooling, and handover architecture. A focused, time -boxed responsibility
(~10–25% early on) that decays toward near -zero as it stabilizes; the
operational coordination of the wind -down sits with delivery/operations. This
is a sunset duty, not a standing one — your gravity is firmly on the
forward -looking product and platform.

Requirements