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Ayush Gupta

AI Engineer building production GenAI, real-time voice-agent, and reliable backend systems

I build AI products beyond the prototype stage: retrieval and generation pipelines, low-latency conversational agents, evaluation systems, provider-neutral AI services, and the backend infrastructure required to run them reliably.

Currently working as an AI Developer at Salescode.ai, where I lead and contribute to enterprise AI coaching, course-generation, and real-time sales simulation systems.

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Production impact

  • Helped deliver enterprise AI coaching workflows serving 10,000+ requests per day.
  • Reduced AI course-generation latency by 60% through reusable templates, context caching, and parallel multimedia processing.
  • Built a streaming STT → LLM → TTS conversational pipeline with response latency below 800 ms in measured production scenarios.
  • Developed automated evaluation and regression coverage across 200 AI workflow test cases.
  • Designed services with tenant isolation, idempotency, retries, heartbeats, stale-job recovery, and event-driven post-call evaluation.

Selected projects

Project What it demonstrates
Mirai Local-first AI image editor with selection-aware generation, reversible history, semantic-fidelity validation, and reproducible diagnostics.
Level Up Coach AI exam coach that turns mock tests into evaluated performance insights and actionable learning recommendations.
Predictor Backend Spring Boot prediction-market backend focused on atomic order placement, idempotency, Redis coordination, and ledger correctness.
Local Gemma Private local AI assistant stack using Ollama, Open WebUI, Docker, and self-hosted search infrastructure.

Engineering focus

  • Generative AI: RAG, LLM orchestration, prompt and context caching, guardrails, structured generation, AI evaluation
  • Real-time AI: LiveKit, WebRTC, streaming STT–LLM–TTS, interruption handling, multilingual conversations
  • Backend systems: FastAPI, Spring Boot, REST APIs, microservices, concurrency control, idempotency, event-driven architecture
  • Data and infrastructure: PostgreSQL, Redis, Kafka, Pinecone, S3, SQLAlchemy, Docker, AWS
  • Languages and frontend: Python, Java, TypeScript/JavaScript, SQL, React, Flutter
  • Quality: Pytest, Vitest, Playwright, regression testing, system design, observability, failure recovery

What I care about

I am particularly interested in AI systems where model quality is only one part of the problem: latency, grounding, evaluation, safety, cost, state management, and operational reliability matter just as much.

I am open to Generative AI, Applied AI, Voice AI, and AI platform engineering opportunities in Bengaluru, particularly with product-focused AI startups.

Contact

The best way to reach me is through LinkedIn.

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AI engineer building production GenAI, voice-agent, and reliable backend systems.

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