Emanuel Otel

Working on Spirax Sarco's MiM chatbot, an enterprise AI assistant for internal knowledge retrieval, support, and document-based question answering.

• Lead observability implementation across the MiM platform, adding trace instrumentation in code and guiding the dev team on debugging, QA, and trace-based root-cause analysis.
• Designed evaluation frameworks comparing MiM against Microsoft Copilot and other AI systems, measuring retrieval quality, answer quality, grounding, and user-facing behavior.
• Generated large-scale synthetic evaluation datasets from ~4,000 PDFs, producing ~280,000 QA pairs for retrieval and answer-quality benchmarking.
• Built end-to-end chat enrichment pipelines for query classification, intent detection, and downstream routing into retrieval and analytics workflows.
• Designed feedback-to-action workflows turning raw user feedback into structured triage, root-cause analysis, and Jira-ready improvement items.

Led AI initiatives across two major clients:

Founding Engineer working directly with the CEO and CTO. Built and launched two AI products from scratch:

• ChatFusion - LLM-powered chatbot platform processing thousands of messages daily. Built the MVP, led deployment, and onboarded major clients, including one of the largest addiction treatment providers in the US.
• VoiceFusion - Speech-to-speech voice AI platform integrating STT, TTS, and LLMs, scaled to thousands of calls per month. Built the PoC and MVP that became the foundation for production voice AI deployments used by US lending companies.
• Built LLM agents with RAG, data extraction, call forwarding, function calling, and multi-step reasoning.
• Increased chatbot lead generation by 28%, improved engagement by 15% through A/B testing, and reduced LLM error rates by 23% via LLMOps, prompt evaluation, and curated fine-tuning workflows.
• Built internal tools for automation, data scraping, and prompt templating to accelerate development cycles.
• Worked with clients, Sales, Product, and Engineering to ship quickly, deliver measurable results, and close feedback loops.

• Implemented deep reinforcement learning models (VAE + LSTM + A3C) for visual navigation in mobile robots.
• Achieved up to 4x performance improvements through model and pipeline optimization.
• Improved image encoding for VAE-based architectures by up to 1% in data reconstruction quality.

• Built large-scale airport data scrapers with BeautifulSoup and Playwright for 300+ airports, automating ingestion into SQL pipelines.
• Delivered internal APIs and dashboards powering aviation pricing insights and decision tools.

• Developed control system software for industrial automation using DeltaV.
• Designed operator interfaces and ran SATs for international clients to validate safety-critical systems.