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Software Development Intern — Cloud & DevOps (AWS)

Vulcury Venture Studio and Advisors
Remote

Quick apply

Job details

Full-time | Internship

Qualifications

Cloud infrastructure
Node.js
AWS
S3
DynamoDB
RDS database
Communication skills
Python

Full job description

This is a remote position.

Software Development Intern — Cloud & DevOps (AWS)

Trustbridge | Full-time Internship | India-based | Paid Stipend

About the Role

Trustbridge is Vulcury's AI-enabled manufacturing supplier matching platform. Our development team is strong on core application logic but needs to build deeper strength in cloud infrastructure — specifically in using AWS to scale production workloads and replace serial processing with parallelized, cache-aware execution.

You'll start on our matching engine, where we currently make repeated, serial calls against a large historical project corpus during supplier scoring. Your first project will be re-architecting how those calls are made — batching, parallelizing, and caching intelligently on AWS — so the engine scales without linearly scaling cost or latency. From there, you'll expand into general infrastructure support across Trustbridge's other components (RFP/RFQ tooling, supplier assessment site, referral/payment tracking).

What You'll Work On

Profile and diagnose the current historical-corpus query pattern in the matching engine to identify where serial calls can be parallelized or cached.
Design and implement AWS-based solutions (e.g., Lambda concurrency, SQS/Step Functions for parallel fan-out, ElastiCache or DynamoDB caching layers) to reduce redundant repeat calls.
Build monitoring and cost-visibility into scaling changes so improvements are measurable, not assumed.
Partner with our Director of AI/ML Software Engineering to align infrastructure changes with the matching engine's scoring logic (corporate fit, capability profile, historical project corpus).
Extend infrastructure practices to other Trustbridge components as the internship progresses.
Support migration of existing workloads and services to AWS — assessing what to lift-and-shift vs. re-architect, planning data migration and cutovers, and validating performance and cost post-migration.
Document architecture decisions and run books so improvements are maintainable beyond the internship.

What We're Looking For

We hire on four traits above all else, and we screen for them as rigorously as technical skill:

Communication — clear speaking, writing, and listening, and a sense of timing for when to raise something.
Hard work and reliability — you follow through and can be counted on.
Willingness and ability to learn — genuine intellectual curiosity, not just credentials.
Positive attitude — you bring people up, not down.

On the technical side:

Solid programming fundamentals (any modern language; Python or Node.js a plus).
Exposure to AWS core services (Lambda, EC2, S3, RDS/DynamoDB, SQS/SNS, or similar) through coursework, personal projects, or prior internships.
Working knowledge of cloud migration concepts — lift-and-shift vs. re-platforming vs. re-architecting, data migration approaches, and minimizing downtime during cutover (exposure through coursework or projects is fine).
Understanding of the difference between serial and parallel execution, and curiosity about caching strategies and when they apply.
Comfortable working independently against written specs and asking sharp questions when specs are ambiguous.

How We Work

Trustbridge runs lean: a senior AI/ML engineering lead sets technical direction, and interns execute against clearly documented SOPs and specs with AI-assisted tooling in the loop. You'll have a daily touchpoint with our Chief of Staff and direct technical guidance from our senior engineering lead — you won't be thrown in without support, but you will be expected to own your workstream.

Details

Location: India-based, full-time.
Compensation: Paid stipend (details discussed during interview).
Reports to: Director of AI/ML Software Engineering, with daily coordination through the Chief of Staff.

To Apply

Send your resume and a short note on a project where you parallelized, cached, or otherwise optimized something that was previously running serially — tell us what was slow, what you changed, and what got better.

Quick apply

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