- Design, develop, and improve agentic AI systems using LangGraph and LangChain, focusing on reliability, traceability, and measurable output quality.
- Advance existing capabilities including DAG driven AI and chatbots by improving reasoning, tool-calling accuracy, and inference confidence.
- Develop and mature evaluation frameworks to assess AI-generated outputs across dimensions.
- Improve and extend multi-LLM ensemble systems including consensus scoring methods, model weighting, and aggregation strategies.
- Design and implement fine-tuning and prompt optimization pipelines for domain-specific cybersecurity and compliance use cases.
- Develop AI/ML components for RAG systems, including embedding strategies, retrieval optimization, chunking, and re-ranking.
- Partner with Product and Engineering teams to identify high-leverage opportunities to introduce AI across workflows.
- Develop and document reusable AI-native components, integration patterns, and deployment blueprints.
- Define and promote standards for responsible AI integration: evaluation methodology, explainability, audit logging, data privacy, and model governance.
- Act as an internal AI advisor helping Product teams navigate model selection, agent design, RAG implementation, and output quality.
- Contribute to building the organizational muscle for AI adoption: documentation, knowledge transfer, and structured learning.
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