B2B EDI Specialist

eClerx•Austin, TX

3d•$145,000

About The Position

The B2B EDI Specialist brings deep B2B/EDI expertise to the design and delivery of AI-enabled integration and operations solutions. This hands-on, stakeholder-facing role supports partner onboarding and end-to-end transaction flows while advancing AI-assisted use cases such as mapping, error analysis, anomaly detection, and operational intelligence. The specialist serves as a subject-matter expert on B2B/EDI standards, document types, and trading partner requirements. They translate business and partner needs into functional and technical specifications, support onboarding from requirements gathering through go-live, and work with internal and external teams to resolve issues. The role also collaborates with business, product, engineering, and data/AI teams to define and prioritize use cases, evaluate AI outputs, communicate progress and risks, and document reusable processes and best practices. This is a fixed-term position for six months, benefits eligible, with any extension subject to business needs and approval. It is located in Austin, Texas. The target base salary in the United States is up to $145,000; compensation depends on factors including relevant experience, knowledge, skills, job-related qualifications, and geography. eClerx provides productized services that combine people, technology, and domain expertise. Its services include business process management, automation, and analytics for enterprises across multiple industries. The company employs more than 19,000 people across numerous countries. eClerx is an Equal Opportunity Employer and is committed to protecting personal data.

Requirements

6–7 years of experience in B2B/EDI integration, onboarding, or support (adjusted to level).
Strong working knowledge of EDI standards (X12 and/or EDIFACT) and common transaction sets.
Hands-on experience with at least one B2B/integration platform, such as IBM Sterling, Seeburger, OpenText, Cleo, SPS Commerce, MuleSoft, or Boomi.
Experience with end-to-end transaction troubleshooting and root cause analysis.
Strong analytical skills and comfort working with data, using SQL or equivalent.
Excellent communication and stakeholder management skills.
Domain depth with a practical, problem-solving mindset.
Curiosity and ability to translate business problems into AI use cases.
Attention to detail and data accuracy.
Ability to collaborate across business, product, and technology teams.
Ownership and ability to work in fast-paced, evolving environments.

Nice To Haves

Exposure to AI/ML or GenAI use cases in integration or operations, including prompt design, LLM-assisted mapping, copilots, or agents.
Familiarity with anomaly detection concepts, monitoring tools, and BI/dashboarding such as Power BI, Tableau, or Splunk.
Knowledge of APIs, JSON/XML, and integration patterns such as REST, SFTP, AS2, and VAN.
Basic Python scripting skills for data analysis or prototyping.

Responsibilities

Serve as the subject-matter expert on B2B/EDI standards, document types, and trading partner requirements, including X12, EDIFACT, XML/JSON, and flat files.
Advise on message specifications, implementation guides, validation rules, and acknowledgement handling such as 997/999 and CONTRL.
Translate business and partner requirements into clear functional and technical specifications.
Support end-to-end partner onboarding, including requirements gathering, mapping specifications, testing, certification, and go-live.
Coordinate with partners, internal integration teams, and support teams to resolve issues and reduce onboarding cycle time.
Identify and drive AI-assisted mapping use cases, including suggesting field mappings, generating mapping specifications, and validating outputs.
Define and support AI-driven error analysis by classifying failures, identifying root causes, and recommending resolutions.
Support anomaly detection relating to transaction volumes, data quality, SLA breaches, and partner behavior.
Contribute to operational intelligence through dashboards, alerts, and insights that improve visibility and decision-making.
Create and curate domain knowledge, labeled datasets, prompts, and test cases to improve model and agent accuracy.
Evaluate AI outputs for correctness and business relevance, and provide structured feedback for continuous improvement.
Collaborate with business, product, engineering, and data/AI teams to define requirements, prioritize use cases, and measure outcomes.
Communicate findings, risks, and progress clearly to technical and non-technical audiences.
Document processes, playbooks, and best practices for reuse.