Key Achievements:

• Architected enterprise multi-agent AI framework using LangGraph and MCP with $35M ARR impact
• Established company-wide standards for agentic AI: security protocols, evaluation metrics, tool-use patterns, and SLM post-training/distillation pipelines
• Implemented meshnet adoption bringing traditional APIs to distributed agent communication systems

Technologies:

Key Achievements:

• Fine-tuned Mistral 7B and Mixtral 8x7B models achieving 94% user satisfaction using PPO reinforcement learning and supervised fine-tuning, compared to initial 65%
• Optimized inference pipelines with INT4-FP8 quantization techniques reducing latency by 60% across heterogeneous GPU hardware
• Implemented spherical interpolation model merging improving multilingual performance by 23% while maintaining base model capabilities
• Deployed production-ready transformers serving 100K+ daily queries with comprehensive monitoring and A/B testing infrastructure

Technologies:

Key Achievements:

• Led technical direction for 5-person engineering team, developing high-EQ conversational AI platform using multi-LLM architecture
• Engineered cognitive-inspired agent architecture for behavioral analysis and emotional intelligence simulation, with autonomous knowledge graph creation
• Employed use of task queues with celery and graph-based agentic systems to create parallel, real-time conversational processing

Technologies:

Key Achievements:

• Built production conversational AI using BERT models, achieving 90% accuracy across 1M+ monthly interactions
• Implemented comprehensive APM monitoring across 100+ microservices using Splunk and OpenTelemetry

Technologies:

Key Achievements:

• Enhanced SVM model accuracy from 79% to 94% through kernel optimization for sentiment classification and cross-border threat detection
• Applied NLP and deep learning techniques to aggregate and process news/social media data for US-Mexico-Canada supply chain assessment

Technologies: