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Senior AI/ML Engineer and technical leader with extensive experience delivering large-scale, production-grade AI systems across healthcare, telecom, and automotive. Specialized in generative AI, graph-based RAG, real-time voice pipelines, and cloud-native MLOps. Proven ability to translate research and engineering into measurable business outcomes—improving reliability, reducing operational costs, and speeding deployment cycles—using AWS/Azure/Kubernetes and modern Python-based architectures.…Senior AI/ML Engineer and technical leader with extensive experience delivering large-scale, production-grade AI systems across healthcare, telecom, and automotive. Specialized in generative AI, graph-based RAG, real-time voice pipelines, and cloud-native MLOps. Proven ability to translate research and engineering into measurable business outcomes—improving reliability, reducing operational costs, and speeding deployment cycles—using AWS/Azure/Kubernetes and modern Python-based architectures.

Daniel Stan

AI Engineer, Data Scientist, DevOps Developer, +2

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Senior AI/ML Engineer and technical leader with extensive experience delivering large-scale, production-grade AI systems across healthcare, telecom, and automotive. Specialized in generative AI, graph-based RAG, real-time voice pipelines, and cloud-native MLOps. Proven ability to translate research and engineering into measurable business outcomes—improving reliability, reducing operational costs, and speeding deployment cycles—using AWS/Azure/Kubernetes and modern Python-based architectures.…Senior AI/ML Engineer and technical leader with extensive experience delivering large-scale, production-grade AI systems across healthcare, telecom, and automotive. Specialized in generative AI, graph-based RAG, real-time voice pipelines, and cloud-native MLOps. Proven ability to translate research and engineering into measurable business outcomes—improving reliability, reducing operational costs, and speeding deployment cycles—using AWS/Azure/Kubernetes and modern Python-based architectures.

Available to hire

Senior AI/ML Engineer and technical leader with extensive experience delivering large-scale, production-grade AI systems across healthcare, telecom, and automotive. Specialized in generative AI, graph-based RAG, real-time voice pipelines, and cloud-native MLOps.

Proven ability to translate research and engineering into measurable business outcomes—improving reliability, reducing operational costs, and speeding deployment cycles—using AWS/Azure/Kubernetes and modern Python-based architectures.

Skills

Experience Level

Expert

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Intermediate

Beginner

Work Experience

Senior AI/ML Engineer | Technical Lead at Tandem Health

January 1, 2024 - Present

Led architecture and technical system design of a real-time voice-first healthcare AI system using AWS Bedrock and agent tooling (AgentCore, LangChain, LangGraph). Implemented FastAPI APIs in Docker on AWS to improve conversation speed and reliability. Built a graph-based RAG system with Neo4j for structured clinical retrieval and multi-hop reasoning. Designed and integrated MCP tools for standardized, secure access to external services, knowledge sources, patient data, and internal APIs for dynamic multi-step workflows. Fine-tuned domain-specific healthcare LLMs for reasoning accuracy, safety alignment, and dialogue consistency. Developed a real-time voice pipeline using LiveKit, Deepgram (STT), and custom TTS for sub-second responsiveness. Established AWS end-to-end MLOps with model versioning, CI/CD, and automated retraining; added Prometheus/Grafana observability; optimized inference with vLLM and CUDA techniques; and contributed to Next.js/TypeScript frontend integration.

Artificial Intelligence Engineer at Amdocs Limited

January 1, 2020 - December 1, 2023

Developed a large-scale telecom anomaly detection system for T-Mobile to reduce manual inspection workload and operational costs. Built data pipelines in Databricks and Azure to transform unstructured engineering documents into structured datasets for downstream analytics. Created document-intelligence solutions using Azure Document Intelligence and Hugging Face LLMs to automate key information extraction and improve accuracy. Developed 3D bounding box detection models with PyTorch/ONNX (reported ~22% precision improvement). Built hybrid search using FAISS and Pinecone to reduce search time by 60%+. Implemented NLP pipelines (Python, spaCy, FAISS) for classification, entity extraction, and discrepancy detection to reduce manual review. Implemented scalable MLOps on Azure Machine Learning with automated testing and CI/CD to speed releases.

Machine Learning Engineer at Luxoft

September 1, 2018 - December 1, 2019

Developed a driver-monitoring system using CNN/RNN models in TensorFlow and PyTorch, integrating real-time video analysis to detect distraction and improve safety alerts. Built ADAS computer vision pipelines, reducing inference latency by 30%. Designed recommendation algorithms leveraging Elasticsearch for personalized content. Developed anomaly-detection models using FAISS and RNN architectures to reduce system failures by 10%. Deployed ML services on AWS using Docker and Kubernetes for reliable continuous updates.

Software Engineer at Zitec

August 1, 2012 - July 1, 2016

Developed backend services using Python, Django, and Flask with modular components to support core product features, enabling faster rollout and improved reliability. Optimized PostgreSQL/MySQL performance through query refactoring and indexing to reduce average latency. Built RESTful APIs and integration services using Django REST Framework to streamline data exchange and reduce integration errors. Implemented asynchronous background processing with RabbitMQ and Celery for tasks such as email notifications and data pipelines, increasing throughput and freeing web request threads.