AI Developer jobs in US
Position: Senior AI Developer / Engineer
Location: Remote – USA
Job Type: Long Term Contract
Department: Platform Engineering
AI Developer with – Python, Azure APIM, LangGraph, LangChain, GenAI etc.
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.
Qualifications:
9+ 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.
Key Benefits:
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.
Onboarding & Training
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AI can flag outdated or missing documentation based on user queries and gaps in responses.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.
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
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
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?”)
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
Phase 4: Optimization
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
To apply for this job email your details to Shubham@longfinch.com