Overview
On Site
Contract - Independent
Contract - W2
Contract - 6+ month(s)
Skills
artificial intelligence
Job Details
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
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).
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:
Curate and ingest internal and vendor documentation, tickets, change requests, and platform-specific knowledge into the AI system.
Collaborate with platform SMEs to validate and refine AI-generated outputs.
Design and maintain workflows for continuous learning and feedback loops between the AI and engineering teams.
Monitor AI performance and identify areas for improvement in accuracy, relevance, and usability.
Develop prompt templates and usage guidelines for engineers to interact effectively with Copilot.
Ensure compliance with data governance, security, and privacy standards.
Curate and ingest internal and vendor documentation, tickets, change requests, and platform-specific knowledge into the AI system.
Collaborate with platform SMEs to validate and refine AI-generated outputs.
Design and maintain workflows for continuous learning and feedback loops between the AI and engineering teams.
Monitor AI performance and identify areas for improvement in accuracy, relevance, and usability.
Develop prompt templates and usage guidelines for engineers to interact effectively with Copilot.
Ensure compliance with data governance, security, and privacy standards.
Qualifications:
3+ years in platform engineering, DevOps, or technical documentation.
Familiarity with OutSystems, AutoRABIT, Azure APIM, or similar platforms.
Experience with AI/ML tools, prompt engineering, or knowledge management systems is a plus.
Strong analytical, communication, and organizational skills.
3+ years in platform engineering, DevOps, or technical documentation.
Familiarity with OutSystems, AutoRABIT, Azure APIM, or similar platforms.
Experience with AI/ML tools, prompt engineering, or knowledge management systems is a plus.
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.
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
Instant access to historical tickets, change logs, and documentation.
Automated summarization and contextual answers reduce time spent searching for information.
Operational Efficiency
Instant access to historical tickets, change logs, and documentation.
Automated summarization and contextual answers reduce time spent searching for information.
Break/Fix Acceleration
AI can suggest known fixes, identify patterns in recurring issues, and recommend escalation paths.
Reduces dependency on tribal knowledge.
AI can suggest known fixes, identify patterns in recurring issues, and recommend escalation paths.
Reduces dependency on tribal knowledge.
Onboarding & Training
New hires can ramp up faster with AI-guided walkthroughs and contextual answers.
Reduces training overhead for senior engineers.
New hires can ramp up faster with AI-guided walkthroughs and contextual answers.
Reduces training overhead for senior engineers.
Documentation Enhancement
AI can flag outdated or missing documentation based on user queries and gaps in responses.
Supports continuous documentation improvement.
AI can flag outdated or missing documentation based on user queries and gaps in responses.
Supports continuous documentation improvement.
Scalability
AI scales with the team, providing consistent support regardless of team size or turnover.
Strategic Insights
Analyze trends in platform issues, usage patterns, and support gaps to inform roadmap decisions.
3. Outline: AI-Supported Platform Engineering Team Process
AI scales with the team, providing consistent support regardless of team size or turnover.
Strategic Insights
Analyze trends in platform issues, usage patterns, and support gaps to inform roadmap decisions.
3. Outline: AI-Supported Platform Engineering Team Process
Phase 1: Foundation
Hire AI Trainer
Audit existing documentation and ticketing systems
Define taxonomy and tagging standards for ingestion
Establish data governance and access controls
Hire AI Trainer
Audit existing documentation and ticketing systems
Define taxonomy and tagging standards for ingestion
Establish data governance and access controls
Phase 2: AI Enablement
Ingest and structure documentation (internal, vendor, tickets, SOPs)
Train Copilot on platform-specific terminology and workflows
Develop prompt templates for common tasks (e.g., "How do I deploy to OutSystems staging?")
Ingest and structure documentation (internal, vendor, tickets, SOPs)
Train Copilot on platform-specific terminology and workflows
Develop prompt templates for common tasks (e.g., "How do I deploy to OutSystems staging?")
Phase 3: Integration
Embed Copilot into daily workflows (e.g., ticket triage, change request reviews)
Pilot with a small group of engineers
Collect feedback and iterate on AI responses
Embed Copilot into daily workflows (e.g., ticket triage, change request reviews)
Pilot with a small group of engineers
Collect feedback and iterate on AI responses
Phase 4: Optimization
Implement feedback loops (e.g., thumbs up/down, correction suggestions)
Monitor usage metrics and accuracy
Expand to additional platforms or tools
Implement feedback loops (e.g., thumbs up/down, correction suggestions)
Monitor usage metrics and accuracy
Expand to additional platforms or tools
Phase 5: Continuous Improvement
Monthly knowledge base updates
Quarterly AI performance reviews
Annual retraining or fine-tuning based on platform evolution
Hari C
Monthly knowledge base updates
Quarterly AI performance reviews
Annual retraining or fine-tuning based on platform evolution
Hari C
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