Technical Focus & Implementation Fields
Analyzing domain requirements, building and training ML classifiers, engineering LangChain agent backends deployed on GCP, and orchestrating autonomous OpenClaw agents.
Implementation Fields & Use Cases
Pragmatic software engineering combined with modern AI tools to solve real business challenges:
RAG & Knowledge Retrieval
Building text embedding and semantic retrieval pipelines using vector stores (Qdrant, Milvus) to ground LLM reasoning in proprietary domain documentation with high accuracy.
Classifiers & Custom ML Training
Developing and training machine learning models, neural networks, and computer vision classifiers (image classification, feature extraction) using PyTorch and Hugging Face models.
LangChain Agents & GCP Cloud Deployment
Engineering custom LLM agent workflows, tool-calling chains, and Python FastAPI backends deployed scalable and serverless on Google Cloud Platform (GCP Cloud Run, Vertex AI) and AWS.
OpenClaw Autonomous Agent Setup
Configuring and orchestrating OpenClaw agent environments with Model Context Protocol (MCP) integrations, shell execution capabilities, and multi-step task planning.
Recommended Tech Stack
Tools & Production EnvironmentsGCP Cloud Run, Google Vertex AI
AWS Lambda, Amazon Bedrock
LangChain, OpenClaw, FastAPI
Model Context Protocol (MCP), A2A Protocols
PyTorch, Hugging Face Transformers
Computer Vision & Classification Models
Qdrant, Milvus
Dense Text Embeddings & Hybrid Search