Owns reference architectures across multiple concurrent AI engagements; sets patterns for agentic, multimodal, and SLM-based systems.
Transformers & Deep Learning
Deep practical grounding in transformers, fine-tuning (SFT, LoRA/QLoRA, RLHF/RLAIF), distillation, quantization, and SLM design for cost/latency-bound deployments.
Generative AI (LLMs & Multimodal)
Designs hybrid multimodal RAG, KAG, and grounded-generation systems; selects the right model class (frontier vs. SLM vs. fine-tuned) per use case.
Architects multi-agent orchestration, planning, memory, tool use, and inter-agent communication patterns including MCP and A2A.
Eval, Safety & Guardrails
Establishes evaluation frameworks, hallucination control, red-teaming, regression suites, and observability as a first-class engineering discipline.
Information Retrieval & Knowledge
Designs hybrid dense–sparse retrieval, ranking, KG-augmented retrieval, and unified knowledge layers across vector, graph, and NoSQL stores.
Predictive & Classical ML
Strong foundation in classical ML and DL (CV, NLP, time-series, GNNs); able to choose non-GenAI approaches when they fit better.
Architects multi-turn, multilingual, multimodal dialogue systems with grounded responses and structured evaluation.
Drives enterprise deployment patterns — cloud, on-prem, and edge — including GPU/accelerator ops, scaling, CI/CD, and lifecycle automation.
End-to-end model and agent lifecycle on AWS/Azure/GCP; cost, latency, and reliability optimization at production scale.
Data Engineering & Pipelines
Designs streaming and batch pipelines (Kafka, Spark, Flink) and the data foundations that production AI systems depend on.
Sets the technical bar for hiring, mentoring, code/architecture reviews, and reusable IP; grows the AI practice as a craft.