You don't need another data scientist who thinks deployment is a Docker tutorial. You need an MLOps engineer who builds feature stores with point-in-time correctness, wires CI/CD for model artefacts, ships canary releases without breaking traffic, instruments drift monitoring that actually pages someone, and keeps the inference bill under the training bill. That's the practice we staff.
$25/hr
Starting rate
3 days
Free PoC delivery
10M+
Daily inference at scale
Classical ML, LLM, or both? Greenfield platform or rescue? Brief us in 60 seconds — we'll match a senior MLOps engineer in 24 hours and ship a working pipeline in 3 days, free.
Replies within 4 business hours · No agency fee
Six engagements from the last twelve months. None are "we wrote a Dockerfile and called it a deployment."
Feast or Databricks Feature Store with point-in-time correct joins, online / offline parity, streaming features via Flink or Bytewax, feature lineage and versioning, and access controls for governance. Typical latency targets: sub-10ms online reads at p99.
Kubeflow / Airflow / Prefect / Databricks Workflows pipelines with parameter sweeps, hyperparameter tuning (Optuna, Ray Tune), data validation (Great Expectations), artefact registry (MLflow), reproducibility via deterministic seeds and pinned dependencies.
KServe / Seldon / BentoML / Ray Serve on Kubernetes, or SageMaker / Vertex endpoints. Canary + shadow deployments, token-aware autoscaling for LLMs, multi-model serving, traffic shadowing for A/B, and sub-100ms p95 latency SLOs.
PSI / KL / Wasserstein drift detectors, prediction distribution alerting, feature-level schema and null checks, SLO-based paging. Runbooks for the three failure modes — retrain, rollback, route-to-human. Dashboards in Grafana, Arize, or WhyLabs.
Self-hosted vLLM / TGI / Ray Serve, PTU or TPU reservations, Langfuse / Langsmith evaluation harnesses, prompt versioning, RAG quality monitoring, guardrails for toxicity and PII, and cost routing (cheap model first, escalate on complexity).
Model cards, datasheets, audit trails on every training run and deployment, dataset lineage via OpenLineage, bias testing with Fairlearn, evidence aligned to EU AI Act, ISO/IEC 42001, and NIST AI RMF. Supports external audit without findings on platform evidence.
Platform-fluent across managed clouds and open-source — we pick the fit, not the preference.
Every project starts with a free 3-day PoC against your real model and data, so you see working pipelines before signing.
30-minute scoping call. We map your current state (notebooks, partial platform, legacy), target cloud, model types (classical / LLM / hybrid), inference volume and latency SLOs, and regulatory posture.
One model end-to-end — features in feature store, training pipeline in CI, model in registry, endpoint deployed, monitoring wired. 30-minute walkthrough of metrics, cost and latency.
Fixed-scope build or dedicated-engineer model. Daily standups in your Slack/Teams, code in your repo, MLOps Asset Bundles or equivalent CI/CD wired from day one.
SLO dashboards, runbooks, cost alerts, and handover to your ML platform team with a governance evidence pack. Or continued fractional engineering if you prefer.
Three engagement models. No GPU resale margin, no platform reseller cut, no minimum term beyond the current sprint.
3 days
Free
One model end-to-end against your real data — feature store, pipeline, endpoint, monitoring. Zero commitment.
10–20 weeks
$80K – $300K
Greenfield MLOps platform or LLM Ops programme. Fixed price, fixed timeline, milestone billing.
Monthly
$25 – $100/hr
Embed a senior MLOps engineer in your platform team. Best when scope evolves or your ML platform is under continuous pressure.
We're not a generalist consultancy with an ML page. Our MLOps practice runs production platforms across hyperscalers and open-source stacks every day.
Every engineer ships a feature store + serving + monitoring exercise during interview. No LeetCode trivia.
We'd rather deploy a working endpoint in your cluster than sell you a capability deck. If the PoC isn't great, no invoice.
We'll tell you when managed endpoints beat self-hosting and when self-hosting with spot GPUs cuts the bill in half. Fit tool to load.
Every engagement includes model cards, audit logs and dataset lineage by default — not bolted on for the regulator.
A Data Scientist explores data and trains models. An ML Engineer writes production model code. An MLOps Engineer owns the platform that takes a trained model and keeps it serving 10M requests a day without drift, latency spikes, or silent failures. That means feature stores with point-in-time correctness, model registries with lineage, CI/CD for model artefacts, deployment patterns (canary, shadow, A/B), autoscaling inference services, drift and data-quality monitors, and cost controls so the FinOps team doesn't sound the alarm. Different discipline, different toolchain.
Yes. Our engineers are platform-fluent rather than platform-loyal. Databricks MLflow + Feature Store + Model Serving for Lakehouse-native teams. AWS SageMaker for deep AWS shops (including Pipelines, Feature Store, Inference Recommender, Model Cards). Google Vertex AI for GCP-centric teams (Pipelines, Feature Store, Endpoints, Model Garden). Azure ML for Microsoft estates. For open-source-first teams we ship MLflow + KServe / BentoML / Seldon on Kubernetes, with Feast as the feature store. We pick by fit, not by preference.
Yes. LLM Ops is where the work is heading — vLLM / TGI / Ray Serve for self-hosted inference, token-aware autoscaling, PTU / TPU reservations, evaluation harnesses in Langfuse or Langsmith, prompt version management, RAG retrieval quality monitoring, hallucination and toxicity guardrails, and cost routing (cheap model → expensive model escalation). We treat classical ML and LLM pipelines with the same operational rigour — the infrastructure patterns are now unified.
Data drift monitoring (PSI / KL divergence / wasserstein), concept drift detection on label delays, prediction drift on inference distribution, feature quality checks (nulls, outliers, schema violations), SLO-driven alerting, and runbooks for the three failure modes: retrain, rollback, route to human. Dashboards land in Grafana or Arize / WhyLabs depending on client preference. Every monitoring system includes golden-dataset regression tests — the fastest way to catch a bad deployment before it hits production.
Feature stores are where 60% of MLOps value lives. We implement point-in-time correct joins, online / offline consistency (Redis / DynamoDB online, Delta / BigQuery offline), feature versioning, lineage, and access controls. Feast for open-source, Databricks Feature Store or Tecton for managed. For real-time features we add Apache Flink or Bytewax for streaming aggregations with checkpointing and exactly-once semantics.
Dedicated engineer from $25/hr (mid-level offshore) to $100/hr (US senior). A typical greenfield MLOps platform build (feature store + training pipeline + model serving + monitoring) is fixed-price $80K–$300K over 10–20 weeks. LLM Ops-specific engagements land $60K–$200K. Every project starts with a free 3-day PoC — one model, end-to-end, deployed to a staging endpoint with monitoring wired in.
Yes. We implement model cards, datasheet-style documentation, audit logs for every training run and deployment, dataset lineage (Great Expectations + OpenLineage + Purview / DataHub), bias and fairness testing (Fairlearn, Aequitas), human-in-the-loop review workflows for high-risk decisions, and compliance evidence packs structured to align with EU AI Act Article 9 risk management, ISO/IEC 42001 AI management system clauses, and NIST AI RMF. We've supported teams through external audits without a single finding on platform evidence.
Brief us on your workload, platform, and pain points. We'll match a senior MLOps engineer in 24 hours and deploy a working pipeline by end of the week — free.