Filter the index on what actually matters.

Retrieval-augmented systems and LLM applications grounded in proprietary data: ingestion, chunking, evaluation, and production serving.

Tool-using agents, workflow automation, and agentic pipelines that execute real back-office work with guardrails and audit trails.

Production ML pipelines with model evaluation, observability, and drift detection, so deployed models stay measurable and reliable.

Visual perception systems for detection, segmentation, classification, and OCR, built from labeling pipelines through deployment.

Structured extraction from contracts, forms, and reports, plus classification and routing that turns document piles into usable data.

Speech-to-text, text-to-speech, and conversational interfaces, including telephony agents measured on containment and handoff quality.

AI evaluation, roadmap, and procurement support from providers verified through shipped work rather than slide decks and frameworks.

Pipelines, feature stores, vector stores, and retrieval infrastructure: the data layer that decides whether an AI build performs.

Domain-specific fine-tunes, LoRA and QLoRA adapters, and distillation runs with evaluation harnesses that prove the lift over base models.

Policy, risk, audit, and regulatory implementation for AI deployments, from internal governance frameworks to regulator-ready documentation.

Internal developer platforms, golden paths, and self-service infrastructure abstractions that cut lead time for product teams.

SLO and SLI design, error-budget policy, load-test-verified capacity, and disaster-recovery plus chaos engineering as evidence.

Cluster operations, GitOps, service mesh, and multi-cluster, multi-region control planes run as verifiable production practice.

Warehouse and lakehouse implementation, ELT, data modeling, and semantic layers that make analytics dependable at scale.

Pipeline reliability, data contracts, freshness and SLA monitoring, and incident response for the data layer the business runs on.

Redshift, Snowflake, BigQuery, and Databricks migrations executed with load verification, so cutover claims come with evidence.

On-prem to cloud and cloud to cloud migrations across refactor, re-platform, and re-host paths, verified against workload baselines.

Cost visibility, chargeback and showback, commitment optimization, and unit-economics reporting that ties cloud spend to outcomes.

Kafka, Flink, and Spark Streaming pipelines with delivery-guarantee attestation, built for exactly-once and low-latency workloads.

Model-serving infrastructure, inference platforms, and model registry plus rollout: the runtime layer under a production AI build.

SOC 2 Type I and Type II audits with evidence collection and control implementation for service organizations facing enterprise procurement.

ISMS design, control mapping, internal audit, and certification-body engagement, taken from gap assessment through certificate.

Data Protection Officer as a service: DPIAs, records of processing, and cross-border transfer assessments handled as an ongoing program.

Risk classification, technical documentation, and conformity assessment for AI systems regulated under the EU AI Act.

HIPAA Privacy and Security Rule readiness, BAA governance, and breach preparedness for organizations handling US healthcare data.

QSA-led PCI DSS scoping and assessment for organizations handling cardholder data, from gap analysis to report on compliance.

AI management system implementation and certification against ISO/IEC 42001, covering governance, lifecycle controls, and audits.

Third-party risk programs: vendor due diligence, contract risk assessment, and ongoing monitoring that stands up in procurement review.

Scoped external, internal, and cloud penetration tests where findings are closed and re-tested, with the retest kept as evidence.

Adversary-emulation engagements driven by real TTPs, scored on measurable detection uplift rather than a slide of critical findings.

Detection engineering, purple-team exercises, and SIEM and SOAR content development that provably raises detection coverage.

IR retainers, drill execution, tabletop exercises, and forensics with post-incident review, tested before the real incident hits.

Around-the-clock monitoring and MDR or MSSP services, with MTTR and MTTD tracked as verified outcomes instead of brochure claims.

Hypothesis-driven threat hunting with TTP coverage measured against MITRE ATT&CK, reported as coverage gained per hunt cycle.

Vulnerability management programs with SLA-based remediation, exposure management, and continuous attack-surface monitoring.

AppSec programs, secure-SDLC uplift, SAST and DAST tuning, and developer security enablement measured by fix rates, not ticket counts.