I'm Adam Bates, a senior software engineer working on backend systems, integrations, and developer tooling.

At JPMorgan Chase, I built Java/Spring Boot services for order management and routing and independently implemented Kafka messaging. I built searchable order auditing so engineers could follow a reported problem through the sequence of events, and led a database migration to AWS with data validation and a team-reviewed cutover after market close.

At Capital One, I joined without prior knowledge of the ATM architecture. I learned how the application, vendor platform, host, and hardware worked together, then used that understanding to lead technical work and independently design and build a Python validation platform.

The platform gave engineering, product, legal, and hardware teams shared test evidence. Visual and functional differences became structured defect reports supporting reviewed fixes, rebuilding, and revalidation. I also set the integration approach between the shared Vue.js library and ATM transaction flows, so the UI team could keep developing its library while the ATM team handled styling and kept the flows stable. The wider modernization remained in progress when I left.

The connection between those roles is the kind of work I want to keep doing: understanding how a system behaves, following problems across its boundaries, and building software that makes it easier for other people to work with.

Core stack: Java, Spring Boot, Kafka, SQL, Python, and AWS. My M.S. in Applied Physics and Computer Science also informs how I compare alternatives and test what a result actually supports.

• JPMorgan Chase: order routing, diagnostic evidence, and migration
• Capital One: learning the system and making its behavior visible

Download the professional portfolio (PDF).

These accounts separate my contribution, the team's work, and the limits of the result. I'm happy to walk through the decisions behind either one. Connect with me on LinkedIn.

These projects show how I investigate failures, make design decisions, and verify results. They are independent implementations, separate from employer systems.

Reliable command processing requires more than receiving a message. I built separate service and simulated-device processes with durable journals to explore duplicate delivery, lost completion responses, and recovery. The service inspects journal evidence before deciding whether to retry; unresolved physical outcomes require inspection. This is a simulation, not a claim of exactly-once physical execution or power-loss safety.

Explore the recorded timeline · Read the code and run it locally · Inspect the crash tests

I compare greedy placement and seeded genetic search against exhaustive enumeration under shared capacity, deadline, and cost constraints. The experiments expose assignments and the gap from the optimum, so the result can be assessed against its configuration and search budget.

This is an independent October 2026 implementation. My 2022 master's thesis addressed a richer fog-network placement problem; it is separate research. Read my thesis note.

Explore the recorded experiments · Read the model and run it locally · Inspect the measurements

Both viewers show recorded output from local runs. Each repository also starts with python run.py for fresh experiments without a cloud account. I use AI assistance and verify the work through explicit behavior contracts, tests, and reproducible experiments. How the illustrations are made.