Applied AI Engineer · Boston

systems forreal-worldworkflows.

I build and evaluate AI systems for real organizational workflows. My current work focuses on agent systems, tool use, evaluation, workflow automation, guardrails, observability, structured data, and human oversight.

Explore my work
GitHub Resume

Selected work

Explore the work.

Agents. Evidence. Human oversight.

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01 / Flagship · Applied AI engineering

Agentic Analytics Lab

When does more agent complexity earn its place?

Concept visual
Editorial illustration for Agentic Analytics Lab; a conceptual visual, not a product screenshot.

289 tests · Python 3.11 / 3.12

Oracle-routed comparison; execution-layer evidence only.

Read case study GitHub
Project notes
Problem
Analytics agents can sound correct while violating the data or metric boundary.
What I built
A guarded ClickHouse analytics agent, deterministic evaluation layer, and separate single-agent, routed-execution, and natural-language routing lanes.
Measured evidence
289 tests on Python 3.11/3.12 at commit 58add5e (October 5, 2026) · recorded CI and CodeQL checks · reviewed 12/18 baseline vs. 17/18 oracle-routed comparison, explicitly bounded to execution-layer evidence.
01 / 05

Selected projects, public implementation, explicit limits. Also: Closing the Loop

How I work

I care about what happens after the demo.

Useful AI needs a baseline, a boundary, and a way to learn from failure.

Baseline first

Start with the simplest credible system, define success, and make added complexity earn its place.

Bound the tools

Permissions, data scope, semantic rules, and approval gates are part of the architecture.

Separate the measures

Execution, correctness, grounding, routing, latency, and cost should not collapse into one score.

Learn from failure

Stable failure patterns are evidence. They should shape the next experiment, not disappear from the story.

Current context

Applied AI Engineering ·
AI for Knowledge Work

Alongside my engineering work, I serve as AI Programs & Operations Coordinator at the Paul English Applied Artificial Intelligence Institute at UMass Boston. The role gives me a close view of how people learn, adopt, and operationalize AI, and why technical systems must make sense inside the organizations around them.

About

Engineering with a wider systems view.

My path into applied AI is interdisciplinary: psychology, analytics, information systems, operations, and hands-on system building.

That mix shapes the questions I ask: Can the system stay inside its boundary? Is its output grounded? What happens when a tool fails? When should a person remain in control? And what evidence would justify the next layer of complexity?

I am completing an MBA at UMass Boston. It supports my focus on applied AI engineering rather than replacing it.

Let’s talk

Useful AI.
Clear evidence.

Email Ajanee LinkedIn GitHub