Andrew G.

Technical Product Lead | LLM Systems & ML Infrastructure | MLOps

AI product tech lead with a background in applied machine learning and systems architecture. Specializes in the productization of large language models (LLMs), recommendation systems, and computer vision pipelines. Fluent in the trade-offs between model fidelity, inference latency, compute cost, and statistical accuracy. Experienced in translating experimental research papers and half-trained checkpoints into production-grade, distributed systems. Operates at the intersection of prompt engineering, data ontology, and backend infrastructure.

###Technical Strategy & Systems Thinking

• LLM Architecture Strategy: Roadmapping across fine-tuned OSS models (Llama 3, Mistral), closed-weight APIs (OpenAI, Anthropic), and hybrid routing layers. Defining context window utilization, retrieval-augmented generation (RAG) chunking strategies, and embedding model selection (e.g., Ada vs. Cohere vs. SBERT).
• ML Evaluation & Validation: Designing offline/online evaluation frameworks beyond accuracy—specializing in hallucination rate, perplexity, toxicity filters, and adversarial robustness. Experience with human-in-the-loop (HITL) labeling workflows and active learning loops.
• Infrastructure & MLOps: Defining requirements for feature stores, model registries, and inference orchestration. Deep understanding of GPU/TPU utilization, autoscaling policies, and cold-start mitigation. Familiar with Kubernetes, Ray, and vector database sharding strategies.
• Data-Centric AI: Prioritizing data curation over architecture tweaks. Expertise in synthetic data generation, class imbalance correction, and weak supervision (Snorkel/Skweak) for low-resource domains.

###Technical Stack & Implementation Fluency

• Languages & Querying: Python (scripting, data analysis), SQL (complex aggregations, feature engineering), GraphQL/REST.
• Frameworks & Libraries: LangChain, LlamaIndex, Hugging Face Transformers, PyTorch, TensorFlow, Scikit-learn, spaCy.
• Infrastructure & Tooling: AWS SageMaker, Bedrock; GCP Vertex AI; Databricks; Weights & Biases; MLflow; Docker; Kubernetes.
• Vector/NoSQL: Pinecone, Milvus, Chroma, Redis, PostgreSQL (pgvector).
• Experimentation: A/B testing with inference shadows, canary deployments, multi-armed bandit algorithms.

###Technical Implementation Highlights