Model development & training: Develop, train, fine-tune, and iterate on ML and NLP models; choose appropriate architectures, prepare training data, run controlled experiments, and improve models based on measured results.
RAG & retrieval systems: Build retrieval-augmented generation pipelines, including document ingestion, chunking, embeddings, indexing, retrieval, and reranking; evaluate retrieval quality and how well generated answers are supported by retrieved evidence.

Agentic pipeline development: Design and implement workflows that combine models, tools, retrieval, and validation for annotation, information extraction, document processing, and other tasks; build reliable orchestration, failure handling, and human-review mechanisms.

Knowledge graph construction: Build pipelines that extract entities and relationships from text, resolve duplicate or ambiguous entities, and organize information into knowledge graphs; maintain source provenance and evaluate extraction and linking quality.

NER & structured extraction: Develop and improve named entity recognition and token-classification systems; define entity schemas and span-boundary rules, review annotations, and investigate contextual label decisions and extraction errors.

Model QA & benchmarking: Systematically evaluate predictions, identify recurring error patterns, and distinguish model failures from annotation or data issues; maintain benchmarks, compare model versions, track relevant metrics, and communicate gains and regressions.

Dataset selection, curation & labeling: Process, filter, audit, and analyze large text corpora and labeled datasets; evaluate candidate sources and select diverse, representative, entity-rich samples; create, review, and correct annotations with careful judgment on ambiguous cases.

Annotation policies & data readiness: Define and refine entity definitions, span boundaries, precedence rules, and edge-case policies; keep labeling rules consistent, and clean, normalize, validate, and convert outputs into training-ready datasets.

Deployment & automation: Integrate ML models and LLMs into services and automated pipelines; implement batching, retries, logging, result collection, and quality checks, and manage trade-offs between accuracy, latency, throughput, and cost.

AI security & guardrails: Build guardrails for LLM and agentic systems, including input and output validation, defenses against prompt injection and jailbreaks, limits on tool access and permissions, and filtering of harmful or policy-violating content; red-team models and pipelines, and measure how well safeguards hold without blocking legitimate use.