Open to opportunities

Timothy Todd

@timothytodd

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Gen AI Architect and ML Engineer specializing in agentic systems, RAG, and multi-LLM integration.

United States

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What I'm looking for

I seek senior roles building production GenAI systems where I can architect scalable, safe agentic AI, mentor teams, and drive end-to-end ML infrastructure adoption.

I am a Gen AI Architect and ML Engineer with 12+ years designing and deploying large-scale AI/ML systems, specializing in agentic architectures, RAG pipelines, and multi-LLM integration.

I have hands-on expertise with Google Vertex AI, LangChain, LlamaIndex, open-source LLMs, and production ML pipelines—building inference services, multi-agent orchestration, CI/CD for ML, and scalable training on hybrid clouds.

I focus on AI safety, observability, and cost optimization, mentoring teams and partnering with cross-functional stakeholders to turn research prototypes into compliant, production-grade GenAI solutions.

Experience

Work history, roles, and key accomplishments

GenAI Architect

iOPEX Technologies

Aug 2023 - Sep 2025 (2 years 1 month)

Architected end-to-end agentic AI systems and RAG pipelines integrating Vertex AI and vector DBs, reducing training costs 25% and delivering <60ms inference endpoints; led productionization, observability, and governance for enterprise GenAI solutions.

Vertex AI LlamaIndex RAG Kubernetes fastAPI Prometheus Pinecone Model Fine Tuning

Full Stack Developer

EPAM Systems

Jan 2018 - Jun 2023 (5 years 5 months)

Designed and deployed conversational AI and RAG architectures on GCP (Dataflow, BigQuery, Vertex AI), reduced model deployment time by 40% via IaC/CI-CD templates, and implemented ML observability and drift detection for production LLMs.

Vertex AI BigQuery Dataflow Faiss CI CD Gitlab Distributed Training

Full Stack Developer

Rackspace Technology

Aug 2013 - Nov 2017 (4 years 3 months)

Built ML and ETL pipelines with Spark and Airflow, migrated early ML workloads to GCP, and implemented CI/CD and containerized deployment patterns to support production ML services and monitoring.