ML Platform Architect | DevSecMLOps
I architect production ML infrastructure for regulated industries at enterprise scale; specializing in feature platforms, governance-by-design, and DevSecMLOps patterns that enable velocity without compromising security or observability.
- Multi-tenant ML Platforms : Eliminate siloed infrastructure that fragments model delivery and inflates operational cost. Impact: Cuts time-to-production from 3–4 weeks to 5–7 days while enabling secure, governed reuse across teams without coordination overhead.
- Governance-by-Design Systems : Remove manual compliance reviews that slow experimentation and create audit risk. Impact: Embeds PII controls and lineage directly into pipelines, eliminating manual review steps while preserving developer velocity.
- Unified ML Observability : Shift from reactive firefighting to proactive model reliability. Impact: Correlates performance, drift, and infrastructure health to reduce mean-time-to-recovery by ~90% and prevent customer-impacting outages.
- DevSecMLOps Pipelines : Make security and compliance automatic, not post-deployment gates. Impact: Aligns CI/CD to OWASP/NIST/CIS standards, reducing critical vulnerability escape rates to near-zero without slowing release cycles.
Cloud & Infra: AWS (SageMaker, Glue, Lambda), Kubernetes, Terraform, Docker, MicroK8s, Juju
Data Platforms: Apache Doris, PostgreSQL, DuckDB, Redis, Kafka, Spark, dbt Core, MetricFlow
ML Tooling: Charmed Kubeflow, MLRun, Feature Store patterns, Model registry & lineage, Prometheus & Grafana
ML Frameworks: Scikit-learn, XGBoost, LightGBM | PyTorch, TensorFlow/Keras | LangChain, LlamaIndex, LangGraph, LangSmith
Governance: OWASP ML Top 10, NIST AI RMF, CIS Controls v8, MITRE ATLAS, OPA/Rego
LinkedIn: linkedin.com/in/felix-isaiah
Portfolio: felix-mutinda.github.io
"The best platform architecture solves constraints, not just technical problems."