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We are looking for a Technical Solutions Engineer to bridge the gap between complex client data and our AI platform. You will be the technical anchor during sales and onboarding. You will turn raw customer RFQs and unstructured documents into highly tailored, functioning AI workflows.
The core responsibilities for the job include the following:
Technical Pre-Sales and Demo Engineering:
Build tailored demos using real customer RFQs and technical documents to prove immediate product value.
Work with complex, unstructured documents and data, transforming chaotic enterprise inputs into structured AI training and inference layouts.
Ingest and prepare data by building custom ETL pipelines using Python and native platform APIs.
Implementation and Customer Onboarding:
Implement and adapt specific customer use cases directly during the post-sale onboarding phase.
Validate platform behavior on real-world data, proactively supporting, tracking, and closing gaps between expected and actual AI output results.
Support customers hands-on in applying the AI platform seamlessly within their daily workflows.
Advanced Troubleshooting and Root Cause Analysis:
Diagnose technical issues dynamically across data quality, document processing pipelines, and workflow configurations.
Identify root causes of platform discrepancies and collaborate with engineering to deploy fixes.
Scaling and Product Feedback:
Contribute to product feedback and establish operational best practices based on field discoveries.
Create and maintain documentation, reusable code patterns, and technical collateral to scale the solutions engineering function.
Requirements:
Experience: 3+ years in a technical solutions engineering, implementation, or deployment role within enterprise SaaS.
Customer-Facing Skills: Ability to guide enterprise clients through complex technical onboarding.
Problem Solving: A relentless analytical approach to diagnosing complex software and data workflow issues.
Technical Hard Skills:
Python Mastery: Strong experience writing Python scripts for ETL, data munging, and API integrations.
Document Intelligence: Deep understanding of parsing unstructured data formats (PDFs, TIFFs, scanned RFQs, DOCX).
Data Pipelines: Experience handling data quality issues, regex, tokenization, or vector embedding preparation.
Experience
3-7 yrs
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