Technical Project Manager - AI Data Processing

Summa Linguae Technologies

11h•$3,000 - $7,000•Remote

About The Position

We’re hiring a Technical Project Manager with a hands-on, data engineering skillset to support Data Processing for AI. You’ll translate client requirements into executable validation logic for data processing workflows, support data post processing, and help ensure delivery data is consistent, measurable, and within client metrics and guidelines. This role sits at the intersection of technical delivery, data QA, and automation. You’ll work closely with development teams and support the Operations Team while also writing/maintaining lightweight scripts and queries that turn requirements into quality assurance checks.

Requirements

3 years experience in technical delivery / project coordination in a data environment (data, analytics, ML, QA automation, or platform operations.
Practical comfort with:
Python (scripting for metrics and data validation workflows)
SQL (queries used in automated checks)
Regex (pattern-based validation)
Command line / Windows CMD (bulk file operations)
Strong written communication and the ability to convert fuzzy requirements into precise, testable checks.
Experience working with engineering teams and using tools like Jira to drive execution.
Ability to evaluate options and recommend an approach based on pros/cons, timelines, and maintainability.

Nice To Haves

Experience with speech/audio or text datasets (given WER and annotation agreement use cases).
Familiarity with cloud data workflows (especially AWS S3 concepts like buckets, prefixes, access patterns).
Experience with data labeling/annotation workflows and quality frameworks.

Responsibilities

Own the end-to-end coordination of data deliveries, from intake to validation and handoff.
Work with client data delivered via S3 buckets or direct uploads; ensure correct structure, completeness, and readiness for downstream use.
Translate client guidelines into automated validation using SQL, regex, and supporting scripts.
Create and to compute quality and consistency metrics such as:
WER (Word Error Rate) maintain Python utilities
IAA (Inter-Annotator Agreement)
Additional dataset-level metrics as required
Use Windows Command Prompt for bulk file operations (creating/moving/downloading folders and files) to support processing and delivery workflows.
Partner with internal development teams by writing Jira tickets for platform improvements and bug fixes (requirements, steps to reproduce, acceptance criteria).
Quickly ramp on internal platforms and configuration logic (e.g., worktypes / templates), advising on setup patterns and tradeoffs.
Investigate issues by querying datasets through database tools and producing clear summaries of findings and next steps.