Languages and software engineering
Expert-level Python; strong shell scripting; API and service design; clean, tested, maintainable code; distributed-system fundamentals; and full SDLC ownership.
Models and AI ecosystem
Deep, current expertise with Google Gemini, GPT-class models, LLaMA and other open models; advanced prompt engineering; structured outputs; model selection; fine-tuning; and rigorous evaluation.
Agentic AI engineering
Hands-on production experience with Google Agent Development Kit (ADK), LangChain, custom agent frameworks, tool use, Model Context Protocol (MCP), workflow orchestration, planning, memory, state, guardrails, human-in-the-loop controls, and multi-agent design.
Retrieval and search
Advanced RAG, hybrid and semantic search, embeddings, re-ranking, and vector platforms such as Vertex AI Vector Search, pgvector, Pinecone, or equivalent technologies.
Cloud infrastructure and AI systems
End-to-end experience architecting highly available, scalable AI and software environments across Google Cloud / Vertex AI, AWS, and Azure, including model serving, data integration, IAM, networking, security, observability, and cost management.
Frameworks
Strong command of LangChain, LlamaIndex, or equivalent orchestration layers - together with the judgment to use simpler, more maintainable patterns when appropriate.
MLOps and LLMOps
CI/CD, model and prompt versioning, experiment tracking, automated evaluation, observability, model monitoring, drift detection, rollback, and production incident response.
Data and ML foundations
Strong SQL and data engineering; classical machine learning; neural networks; training, tuning, and inference; and the ability to connect data quality to model and product outcomes.
