2D / Field notes3D / Motion
Ishank Sharma Let’s talk

Forward Deployed Engineer at Prentis AI

Los Angeles, California

The messy part
is my kind
of work.

I’m Ishank. I turn ambiguous problems into AI products that work in the real world. From the first customer conversation to the last mile of production.

Explore my work
A familiar starting point The messy middle of forward deployed engineering Scattered customer asks, people, data, legacy tools, risks, and ownership questions. Make it make sense arranges them into discovery, framing, building, validation, operations, and handoff. A vague askDiscover“Can we add AI?”Find the real need PeopleFrameDifferent prioritiesAgree what matters Legacy toolsBuildHidden dependenciesFit the environment Messy dataValidateWhat can we trust?Prove it in real use UnknownsOperateWhat if it breaks?Recover. Improve. Who owns it?TransferBeyond the demoA team can own it The real brief is rarely a neat brief. Learn in the field. Feed it back. Real peopleLegacy systemsA little AIA lot of unknowns

A little like my day job. Give it a click.

Customer context meets engineering depth.

Applied AIBackend systems0-to-1 ownership

Things I’ve
put into motion.

Useful software starts with a real problem.
Here are a few of mine.

Prentis AI / 2026–present

From possibility.
To product.

Forward Deployed Engineer at Prentis AI, building AI-powered products from discovery to production.

  • Applied AI
  • Product engineering
  • 0-to-1
Three kinds of judgment. One working workflow.AI interprets, code checks, and people decide. Together they support a reliable workflow with evidence, feedback, and a team that can own it. A generic illustration, not a client system. AIInterpret CodeCheck PeopleDecide A workflow that works.Evidence. Recovery. A named owner.

A way of working. Not a client blueprint.

From1 day
To3 min

Same decision. A much shorter wait.

CommerceIQ / Data & infrastructure

Making the waiting disappear.

A media budget recommendation pipeline built with AWS and roughly 40 SQL modules. Processing fell from a day to about three minutes, with approximately $200K in annual client savings.

Step Functions, SQS, Lambda, SQL
Has anyone solved this before?

Let’s start with what
your team already knows.

Ella / Knowledge systems

A better memory for the team.

A Slack assistant that finds answers in company knowledge, escalates unanswered questions, and turns those answers into knowledge the next person can use.

Explore the project

Also on my workbench

Firebolt Geospatial AnalyticsData, maps, and millions of recordsSemantic Data NavigatorFinding meaning in documentsPolice Beats AllocationSmart India Hackathon winnerMock Server FrameworkFaster API prototyping
How I work

Close to the problem.
All the way
through.

The interesting part is connecting the pieces: what people need, what the model can do, and what the system must guarantee.

  1. 1

    Understand the actual work.

    Talk to the people doing it. Trace the workarounds. Find the problem beneath the feature request.

  2. 2

    Use AI where it earns its place.

    Models for ambiguity. Deterministic code for rules. Clear boundaries between the two.

  3. 3

    Own the last mile.

    Integration, evaluation, recovery, and adoption. The job is done when the workflow actually works.

A little about me

Engineer by trade.
Curious by default.

Ishank Sharma in San Francisco
Usually asking one more question.

I like problems that don’t arrive neatly packaged.

My foundation is in backend, data, and distributed systems. At CommerceIQ, I built pipelines, optimized infrastructure, and grew into Forward Deployed Engineering. At Prentis AI, I’m taking on the whole journey: discovery, product, engineering, and production.

I completed my M.S. in Computer Science at CSULB in May 2026. These days, I’m based in Los Angeles, working at the intersection of customer context and applied AI.

Outside the implementation details, I’m drawn to philosophy, psychology, and questions about how people think. There’s usually a good film somewhere in the mix, too.

Full profile & résumé LinkedIn

The journey so far

2026–now
Prentis AI

Forward Deployed Engineer

Discovery to production
2026
CommerceIQ

Forward Deployed Engineer

Analytics & applied AI
2025
Stealth startup

Full Stack Engineer

Text-to-SQL & LLM systems
2021–2024
CommerceIQ

Software Engineer, SDE 1 to SDE 2

Backend & data foundations

Some familiar tools

Python SQL Claude Amazon Bedrock FastAPI AWS PostgreSQL Docker Databricks JavaScript

Built with a competitive streak.

Smart India Hackathon winner. Microsoft IncubateIND and Reverie hackathon runner-up. Honorable mentions at CommerceIQ AI and GE Healthcare.

Thinking out loud

A few ideas
outside the code.

From data visualization to the fourth dimension. Writing is where I work through the questions that stay with me.

Read my writing on Medium