Mactores
via Lever
Generative AI Engineer (Intern)
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 16h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 48d ago
The date the source published, not the day we noticed it (2026-07-29). Last seen at its source 3h ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Mumbai
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $6,750–7,250/mo
Middle 50% of 9 listings that do state pay — Engineering · all levels · India · USD/month. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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lever employer's own board first seen 38d ago · last seen 3h ago
The listing
Mactores is the agent-native AWS modernization firm. Most modernization work doesn't ship, it stalls in pilots, slips a year, or lands at three times the budget. We exist to ship it: production systems running, legacy retired, outcomes measured. Our delivery is built on Aedeon, the agent platform built by Mactores' founders' sister company, which absorbs the repetitive 60–70% of engagement work, discovery, dependency mapping, validation, test generation, that traditional consulting bills human hours against. Forward-deployed engineers own the rest: architecture, judgment, and cutover, on dates we commit to in the contract.
This internship sits alongside our Forward-Deployed Engineers (FDEs), who embed with customer teams and ship production agentic AI systems on AWS. You won't carry a delivery commitment on day one instead, you'll work under an FDE's mentorship to learn how agentic systems get built, tested, and verified against real customer data, and you'll get structured exposure to the judgment calls that sit behind every shipped feature.
This is a learning-and-building role, not a staff-augmentation seat and not an advisory role. You'll write real code that feeds into real engagements, under close supervision.
What You'll Do?
- Support delivery of agentic AI and AWS modernization work across our three pillars: Data Platform Modernization, Application & Database Modernization, and AI Agents for Apps.
- Help build and test components of AI agents, orchestration, retrieval pipelines, evaluation harnesses under FDE guidance, working with real (not demo) data wherever possible.
- Assist in converting existing product or process functionality into callable agent tools, under supervision.
- Sit in on architecture sessions and customer discussions to learn how design decisions get made and defended; you'll observe and contribute, not lead.
- Help document agent decisions and test evidence so outputs are traceable and defensible.
- Bring back what you learn from shadowing engagements into small improvements you can own.
What we're looking for?
- Currently pursuing, or recently completed, a degree in Computer Science, Engineering, or a related field.
- A foundation in Python (and ideally some TypeScript) from coursework, personal projects, or prior internships no professional production experience required.
- Genuine curiosity about agentic AI: how agents plan, use tools, retrieve context, and get evaluated. You don't need to have shipped an agent, you need to want to learn how they're built properly.
- Basic familiarity with cloud concepts; AWS exposure through coursework, certifications-in-progress, or personal projects is a plus.
- Comfort with testing-driven habits, willingness to learn to write test cases before code, not after.