Lead the development of bespoke internal automations and custom applications for the firm, global corporate legal clients, leveraging API integrations to streamline complex workflows.

Write for the firm’s international newsletter, delivering actionable AI insights to a global client base to establish the firm as a premier AI consultancy in the legal and enterprise sectors.

Spearhead the roadmap to scale a specialized engineering team focused on deploying agentic AI applications and sophisticated LLM orchestrations.

Utilize cutting-edge agentic development tools, including Claude Code, to accelerate rapid prototyping and deployment of high-impact legal-tech solutions.

Designed, architected, and implemented a concurrent, distributed, highly scalable Gen-AI RAG Application including a dns collector, web scraper, and html LLM processor service to reduce cognitive load by creating talking points for sales reps to use with potential customers, processing 700,000 web pages and 10,000+ talking points daily.

The primary external technologies I used were Java, Python, Snowflake, Pinecone, MySQL (Vitess), Kafka, SQS, Mistral, Llama, Langchain.

Researched and tested several different inference servers and optimization strategies for internal LLM service on GPU enabled AWS EC2 instances.

The primary technologies I researched and tested were Nvidia’s Triton Server, Ollama, TensorRT, TensorRT-LLM, vLLM, and ONNX standard.

Code reviewed front-end React/Redux (Typescript) application feature code.

Built dashboard features using Python, Django, and ElasticSearch for an NLP focused contract management application. Added detailed observability using Open Telemetry, Prometheus, Grafana, Honeycomb.

Added fronted features to the dashboard using React.

Architected and implemented backend services to retrieve complex aggregations from ElasticSearch.

Initiated and upgraded the Python application and its dependencies from Python 3.7 to Python 3.10.

Uncovered a large, unnecessary database expense. Implemented code to remove 300 million cells of data from a Postgres Database (RDS).

Implemented features related to AWS’s S3, EC2, OpenSearch, API Gateway, Lambda, DynamoDB, and RDS.