

Platform Support AI Trainer
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a Platform Support AI Trainer with a contract length of 6+ months, offering a hybrid work location. Key skills include platform engineering, familiarity with OutSystems, AutoRABIT, and Azure APIM, plus experience in AI/ML tools.
π - Country
United States
π± - Currency
$ USD
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π° - Day rate
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ποΈ - Date discovered
June 4, 2025
π - Project duration
More than 6 months
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ποΈ - Location type
Hybrid
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π - Contract type
Unknown
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π - Security clearance
Unknown
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π - Location detailed
Columbia, SC
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π§ - Skills detailed
#Alation #Documentation #Azure #AI (Artificial Intelligence) #Data Governance #Compliance #Security #API (Application Programming Interface) #ML (Machine Learning) #DevOps #Scala #Base
Role description
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TECHNOGEN, Inc. is a Proven Leader in providing full IT Services, Software Development and Solutions for 15 years.
TECHNOGEN is a Small & Woman Owned Minority Business with GSA Advantage Certification. We have offices in VA; MD & Offshore development centers in India. We have successfully executed 100+ projects for clients ranging from small business and non-profits to Fortune 50 companies and federal, state and local agencies.
Position: Platform Support AI Trainer
Location: Hybrid (Columbia, SC or Remote)
Duration: 6+ months
Summary
We are is seeking a Platform Support AI Trainer to lead the integration of AI into our Platform Engineering operations. This role will be responsible for curating, structuring, and maintaining the knowledge base that powers our AI assistant (Copilot), enabling it to provide accurate, context-aware support for platforms such as OutSystems, AutoRABIT, and Azure API Management (APIM).
Key Responsibilities
β’ Curate and ingest internal and vendor documentation, tickets, change requests, and platform-specific knowledge into the AI system.
β’ Collaborate with platform SMEs to validate and refine AI-generated outputs.
β’ Design and maintain workflows for continuous learning and feedback loops between the AI and engineering teams.
β’ Monitor AI performance and identify areas for improvement in accuracy, relevance, and usability.
β’ Develop prompt templates and usage guidelines for engineers to interact effectively with Copilot.
β’ Ensure compliance with data governance, security, and privacy standards.
Qualifications
β’ 3+ years in platform engineering, DevOps, or technical documentation.
β’ Familiarity with OutSystems, AutoRABIT, Azure APIM, or similar platforms.
β’ Experience with AI/ML tools, prompt engineering, or knowledge management systems is a plus.
β’ Strong analytical, communication, and organizational skills.
Business Case for AI-Supported Platform Engineering
Objective
To enhance platform reliability, reduce MTTR (Mean Time to Resolution), and improve engineering productivity through AI-assisted knowledge management and operational support.
Key Benefits
Operational Efficiency
β’ Instant access to historical tickets, change logs, and documentation.
β’ Automated summarization and contextual answers reduce time spent searching for information.
Break/Fix Acceleration
β’ AI can suggest known fixes, identify patterns in recurring issues, and recommend escalation paths.
β’ Reduces dependency on tribal knowledge.
Onboarding & Training
β’ New hires can ramp up faster with AI-guided walkthroughs and contextual answers.
β’ Reduces training overhead for senior engineers.
Documentation Enhancement
β’ AI can flag outdated or missing documentation based on user queries and gaps in responses.
β’ Supports continuous documentation improvement.
Scalability
β’ AI scales with the team, providing consistent support regardless of team size or turnover.
Strategic Insights
β’ Analyze trends in platform issues, usage patterns, and support gaps to inform roadmap decisions.
β’ Outline: AI-Supported Platform Engineering Team Process
Phase 1: Foundation
β’ Hire AI Trainer
β’ Audit existing documentation and ticketing systems
β’ Define taxonomy and tagging standards for ingestion
β’ Establish data governance and access controls
Phase 2: AI Enablement
β’ Ingest and structure documentation (internal, vendor, tickets, SOPs)
β’ Train Copilot on platform-specific terminology and workflows
β’ Develop prompt templates for common tasks (e.g., βHow do I deploy to OutSystems staging?β)
Phase 3: Integration
β’ Embed Copilot into daily workflows (e.g., ticket triage, change request reviews)
β’ Pilot with a small group of engineers
β’ Collect feedback and iterate on AI responses
Phase 4: Optimization
β’ Implement feedback loops (e.g., thumbs up/down, correction suggestions)
β’ Monitor usage metrics and accuracy
β’ Expand to additional platforms or tools
Phase 5: Continuous Improvement
β’ Monthly knowledge base updates
β’ Quarterly AI performance reviews
β’ Annual retraining or fine-tuning based on platform evolution
Hari C
hari.c@technogeninc.com
+1-703-988-6024