Eight focused practices, each owned by a senior platform engineer. Engage one, engage all — we deliver outcomes, not headcount.
We design ML platforms that survive the next architecture review — multi-cloud, multi-region, with GPU autoscaling and bare-metal options for inference-heavy workloads.
# Reference: GPU node pool with autoscaling spot apiVersion: karpenter.sh/v1 kind: NodePool spec: template: spec: requirements: - key: "node.kubernetes.io/instance-type" operator: In values: ["p5.48xlarge", "g6e.12xlarge"] - key: "karpenter.sh/capacity-type" values: ["spot", "on-demand"] taints: - key: nvidia.com/gpu effect: NoSchedule disruption: consolidationPolicy: WhenEmptyOrUnderutilized budgets: - nodes: "10%"
Inference dominates the modern AI bill. We attack it from every angle — caching, distillation, quantization, smart routing — and get teams from "please ask CFO" to "shipping more features".
Production AI systems fail in subtle ways: drift, latency tails, hallucination spikes, vector index corruption. We build the guardrails that catch them before the war room.
Aggregate across 240 production endpoints we operate.
Lakehouse, feature stores, vector DBs, streaming pipelines — the substrate that decides whether your models are 60% accurate or 95%.
Iceberg / Delta tables on S3, ADLS, or GCS. Snowflake or Databricks for SQL. dbt for transforms. Catalog with Unity, Polaris, or Glue.
Tecton, Feast, Hopsworks. Online / offline parity, point-in-time correctness, governed feature reuse across teams.
Kafka, Pulsar, Kinesis, Flink. Real-time features, event-driven retraining, change-data-capture from OLTP into the lakehouse.
Pinecone, Weaviate, Qdrant, pgvector, Turbopuffer. Tuned for recall, latency, and cost — chosen for your retrieval profile.
Great Expectations, Soda, Monte Carlo. OpenLineage for end-to-end traceability from source table to served prediction.
Active learning loops, RLAIF, synthetic generation for fine-tuning, partnerships with leading labeling vendors.
A model is software. We treat it that way: reproducible builds, signed artifacts, registry-driven promotions, GitOps deploys, and progressive delivery on every change.
In the GenAI era, "did the unit test pass" is not enough. We build evaluation harnesses that run nightly against gold sets, adversarial probes, and live traffic samples — so quality regressions are caught before users see them.
# Example: eval gate in CI $ mlops-eval run \ --model-uri "registry://support-bot/42" \ --suite gold-set,jailbreak,latency \ --baseline "registry://support-bot/41" \ --fail-on-regression 📊 gold-set ......... 94.2% (Δ +0.3pp ✓) 📊 jailbreak ........ 99.1% (Δ +1.4pp ✓) 📊 latency-p99 ...... 612ms (Δ −44ms ✓) 🟢 PROMOTION CLEARED
EU AI Act. NIST AI RMF. SOC 2. HIPAA. GDPR. The frameworks change, the obligations stay: know what your model is, what it was trained on, what it's allowed to do — and prove it.
EU AI Act high-risk classification, model cards, system cards, data sheets — auditable in your regulator's preferred format.
Immutable training run records. Every prompt, response, and tool call logged with retention policies for legal and audit.
Scheduled adversarial probes for jailbreaks, prompt injection, PII leakage, biased outputs. Findings tracked to closure.
Tokenization, regional inference routing, BYOK encryption, customer-managed keys. Approved patterns for HIPAA, FedRAMP, and EU residency.
We've taken three clients through Type II audits with zero ML-related findings. Controls written by engineers, not lawyers.
DPAs with every AI vendor in your stack. Foundation-model usage policies for engineering and legal alignment.
When platform engineers, ML researchers, product, and compliance all want different things, models stall. We've designed and embedded inside enough teams to know which interfaces work — and which don't.
Most engagements start with a single service — cost or reliability — and expand once the wins are visible. We don't oversell.