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I’m an AI/ML engineer specializing in LLM orchestration, conversational architecture, and high-availability model infrastructure. My experience spans building multi-persona AI agents, memory-augmented conversational systems, and real-time inference pipelines with sub-second latency. I’ve deployed production-grade GenAI systems at scale using OpenAI, Anthropic, Meta models, distributed GPUs, vector databases, and Kubernetes—while implementing robust safety layers and alignment frameworks. I enjoy designing end-to-end intelligence stacks that enable emotionally intelligent, immersive, character-driven AI experiences.…I’m an AI/ML engineer specializing in LLM orchestration, conversational architecture, and high-availability model infrastructure. My experience spans building multi-persona AI agents, memory-augmented conversational systems, and real-time inference pipelines with sub-second latency. I’ve deployed production-grade GenAI systems at scale using OpenAI, Anthropic, Meta models, distributed GPUs, vector databases, and Kubernetes—while implementing robust safety layers and alignment frameworks. I enjoy designing end-to-end intelligence stacks that enable emotionally intelligent, immersive, character-driven AI experiences.

Nikolai Vasilev

Data Scientist, AI Engineer, AI Developer, +2

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I’m an AI/ML engineer specializing in LLM orchestration, conversational architecture, and high-availability model infrastructure. My experience spans building multi-persona AI agents, memory-augmented conversational systems, and real-time inference pipelines with sub-second latency. I’ve deployed production-grade GenAI systems at scale using OpenAI, Anthropic, Meta models, distributed GPUs, vector databases, and Kubernetes—while implementing robust safety layers and alignment frameworks. I enjoy designing end-to-end intelligence stacks that enable emotionally intelligent, immersive, character-driven AI experiences.…I’m an AI/ML engineer specializing in LLM orchestration, conversational architecture, and high-availability model infrastructure. My experience spans building multi-persona AI agents, memory-augmented conversational systems, and real-time inference pipelines with sub-second latency. I’ve deployed production-grade GenAI systems at scale using OpenAI, Anthropic, Meta models, distributed GPUs, vector databases, and Kubernetes—while implementing robust safety layers and alignment frameworks. I enjoy designing end-to-end intelligence stacks that enable emotionally intelligent, immersive, character-driven AI experiences.

Available to hire

I’m an AI/ML engineer specializing in LLM orchestration, conversational architecture, and high-availability model infrastructure. My experience spans building multi-persona AI agents, memory-augmented conversational systems, and real-time inference pipelines with sub-second latency. I’ve deployed production-grade GenAI systems at scale using OpenAI, Anthropic, Meta models, distributed GPUs, vector databases, and Kubernetes—while implementing robust safety layers and alignment frameworks. I enjoy designing end-to-end intelligence stacks that enable emotionally intelligent, immersive, character-driven AI experiences.

Skills

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Intermediate

Work Experience

ML Ops Engineer at White Crest Interiors

February 1, 2024 - Present

Architected and implemented a cutting-edge MLOps platform using Kubernetes and Kubeflow, reducing model deployment time by 75% and increasing model performance by 30% across the organization. Led a cross-functional team of 15 engineers to develop an automated ML pipeline with explainable AI features, increasing interpretability and regulatory compliance by 40%. Designed and deployed federated learning adoption across 5 global partners to enable secure multi-party collaboration while preserving data privacy and improving model accuracy by 25%.

Machine Learning Engineer at Quantum Advisory

September 1, 2021 - January 1, 2024

Engineered a real-time model monitoring system using stream processing, reducing drift detection time from days to minutes and boosting reliability by 50%. Built scalable infrastructure for high-frequency model training using hybrid cloud, reducing operational costs by 35% and achieving 99.95% uptime. Developed a custom AutoML solution integrating quantum-inspired algorithms, accelerating model development cycles by 60% and improving performance across diverse use cases.

Data Engineer at Cromwell & Ash

December 1, 2019 - August 1, 2021

Implemented CI/CD pipelines for ML models using GitOps principles, reducing deployment errors by 80% and enabling seamless rollouts for 100+ production models. Engineered a scalable feature store using cloud-native technologies, improving data consistency across 50+ ML projects and reducing feature engineering time by 40%. Collaborated with data scientists to containerize ML workflows, achieving 70% improvement in reproducibility and enabling on-demand scaling of compute resources.