Portfolio · AI platforms

Mohammadreza “Hamid” Matiny

AI Infrastructure & MLOps Engineer specializing in LLM serving, GPU orchestration, and production observability.

Currently: Shipping AI infrastructure and data platforms across 30 public repositories.

About

Platform engineering for models that have to ship.

I’m Mohammadreza (“Hamid”) Matiny — an AI infrastructure and MLOps engineer with 4 years across deep learning systems, data pipelines, software backend systems, and cloud infrastructure on GCP and AWS. My work sits where model code meets the platform: serving contracts, GPU scheduling, experiment provenance, and the observability that makes production behavior trustworthy.

Today I build AI platform infrastructure — LLM serving, GPU orchestration, fleet telemetry, and computer-vision pipelines — alongside agentic-AI security work (policy-as-code gateways, tool-abuse controls, audit trails). That work spans 30 public repositories, 4 of them production-shaped platforms with real CI/CD and Terraform-provisioned infrastructure — and where it matters, measured results instead of estimates: one lakehouse run processed 42,972 records at a 91.8% acceptance rate with zero post-gate rejections.

I care about honest maturity signals: tagged releases when they’re real, architecture diagrams that match the repo, and metrics that come from measured runs — not marketing copy.

4 years Deep learning systems, data pipelines, software backend, and cloud infrastructure

4 platforms Production-shaped systems: multi-backend LLM serving, streaming lakehouses, CV detection

30 repos Public repositories spanning LLM infra, data engineering, and computer vision

Featured work

Case studies, not just repos.

Architecture, engineering decisions, and honest maturity — verified against public GitHub sources.

Vulcan

Active on main

Multi-backend LLM serving and GPU-orchestration platform — one contract across vLLM, Triton, Ray Serve, KServe, and BentoML.

Tagged v1.2.0 across 22 phases: advanced GPU serving (GPTQ/AWQ/FP8, TensorRT-LLM templates), cost-per-token tracking, training backends, LoRA/PEFT, DVC exports, pluggable MLflow/W&B tracking, and a tool-grounded LangGraph advisor with non-fabrication CI.

Architecture

Clients and a LangGraph advisor hit a routing gateway that speaks a unified model-serving contract (/health, /metrics, /v1/infer). Backends — BentoML, Ray Serve, Triton (+ TensorRT-LLM), vLLM (+ GPTQ/AWQ/FP8 packs), KServe — plug in behind that contract. Training jobs (Ray Train, FSDP/DDP, DeepSpeed, LoRA/PEFT) share a TrainingJobSpec; MLflow and W&B track runs; DVC versions exports. GPU infra (Kueue / Karpenter / MIG) is validated in CI without burning real GPU cost. SageMaker and Bedrock are selectable managed paths. Observability (Prometheus, Grafana, Tempo, cost-per-token) grounds every advisor number in real evidence.

Key decisions

Unified serving contract instead of per-backend APIs (ADR-001)
GPU cost-safety: validate-only infra in CI; no invented tokens/s (ADR-002, ADR-007)
Kueue multi-tenant scheduling + MIG partitioning strategy (ADR-003, ADR-004)
LangGraph advisor is tool-grounded — every stated number must appear in Prometheus/benchmark evidence (ADR-014)
Pluggable experiment tracking: MLflow self-hosted + W&B offline-only in CI (ADR-013)

vLLM
Triton
Ray Serve
KServe
BentoML
Kueue
Karpenter
TensorRT-LLM
LoRA/PEFT
DVC
MLflow
W&B
LangGraph
SageMaker
Bedrock

System shape

View repository →

Argus

Active on main

Production-shaped fleet telemetry platform — Kafka/Ray/Flink ingest, Iceberg + Dagster lakehouse, drift detection, OPA-backed incidents, and a read-only AI copilot.

Tagged v1.0.0 — CHANGELOG calls it the first production-shaped release (Phases 0–15). 45 Docker Compose services, 41 test files (including Kafka integration tests), and 6 active CI workflows (ci, docker-build, semgrep, e2e-nightly, load-nightly, chaos-nightly).

Architecture

Redpanda/MSK → Ray ingest → stream-processor QA gate (Flink option) → Iceberg + Trino lakehouse → Dagster/MLflow orchestration → drift-monitor (KS tests, embeddings, Evidently) → OPA-backed incident-engine (circuit breakers) → api-gateway (OIDC/Keycloak, OPA RBAC) → Next.js dashboard, plus a read-only Qdrant-RAG AI copilot with its own eval harness. Same container images run via Docker Compose locally or Terraform + Helm (one chart per service) + Argo CD app-of-apps on EKS.

Key decisions

Contract-first streaming path with an explicit QA gate before lakehouse writes
Iceberg + Dagster for reproducible lakehouse materialization
OPA policy for incident decisions — not prompt-only automation
Copilot is read-only against telemetry and runbooks, backed by an eval harness
Documented scope cuts (KNOWN_GAPS.md) instead of overclaiming — no service mesh/mTLS, Vault-backed secrets, or column-level lineage yet

Kafka
Redpanda
Ray
Flink
Iceberg
Trino
Dagster
OpenTelemetry
OPA
Argo CD
Terraform
Next.js
Qdrant

System shape

View repository →

PRISM

Active on main

Multi-warehouse fleet-intelligence platform — camera/sensor ingest, a PySpark lakehouse with dbt gold models, and OpenCV/ONNX defect detection with human review.

Tagged v1.2.0 — all 20 phases (0–19) complete, including a golden-path chaos e2e test against the live Compose stack. 17-service Docker Compose stack, 19 test files, 2 CI workflows (lint/test, Terraform validate + release packaging).

Architecture

Camera/sensor ingest lands in a bronze zone, then splits: a PySpark medallion lakehouse (bronze → silver → gold, dbt-modeled) on one side, an OpenCV/ONNX YOLO-family CV service on the other, routing low-confidence findings to a Django control-plane review queue. Gold data fans out through one activation contract to both Redshift and Snowflake, mirrors to Azure Databricks/ADLS for DR, and feeds a Vue 3 + Three.js digital-twin cockpit. An incident-engine runs per-asset circuit breakers on OPA/Rego trip policies; Dagster orchestrates the lakehouse and drift-monitor; a tool-grounded AI copilot answers only from evidence it can cite.

Key decisions

Two-layer Pydantic → Pandera validation gate before any bronze promotion
Per-asset circuit breakers driven by OPA/Rego trip policies, not hardcoded thresholds
Drift-monitor baselines never build from synthetic scenario data — health stays non-ready until a real baseline earns it
Copilot is tool-grounded — every answer must cite real evidence (ADR-004)
Cloud paths (AWS + Azure Terraform) are plan/validate/checkov-only in CI; human apply only (ADR-001)

PySpark
Databricks
dbt
OpenCV
ONNX / YOLO
Django
Vue 3 + Three.js
OPA / Rego
Dagster
Terraform
Snowflake
Redshift

System shape

View repository →

FORGE

Active on main

Offline AV perception & auto-labeling platform — 2D/3D detection, tracking, sensor fusion, and active-learning pseudo-labeling over a versioned Parquet data lake.

Tagged v0.2.0 — all 11 phases complete (ingest through visualize plus productionization). 15 test files, 2 CI workflows. Detection heads are randomly initialized research baselines, not trained on real labels — documented honestly in KNOWN_GAPS.md rather than overclaimed.

Architecture

The forge CLI runs an 8-stage pipeline end to end locally: ingest (nuScenes → Parquet lake, DVC, Hydra configs) → detect2d (Faster R-CNN) and detect3d (PointNet-style) → track (SORT: Kalman + Hungarian IoU) → fuse (calibrated projection + IoU) → label (trust scoring + active learning) → evaluate (BEV distance, mAP against held-out ground truth) → curate (LanceDB dedup) → visualize (rerun.io / Foxglove MCAP). A parallel cloud path — S3 → Lambda → SQS → DynamoDB → EventBridge → Step Functions → ECS Fargate → Glue (11 tables) → Athena — is Terraform-defined and structurally verified in CI, but intentionally never applied against live AWS.

Key decisions

Ground-truth labels used only for evaluation, never as a pipeline input — no label leakage
Every pipeline stage round-trips through a versioned Parquet lake instead of ad hoc intermediate files
Cloud orchestration built and CI-verified as code, deliberately never deployed — same cost-safety policy as Vulcan, PRISM, and hydra-data-factory
Ray distributed execution wired for the two heaviest stages (detect2d/detect3d); the rest run local by design
Random-init detectors are labeled as smoke-tested baselines, not tuned models — no inflated accuracy claims

PyTorch Lightning
Faster R-CNN
SORT
Ray
LanceDB
MLflow
W&B
DVC
Hydra
Terraform
Parquet

System shape

View repository →

aegis

Active on main

AI-native defense-in-depth gateway for LLM apps and agents — prompt injection, data exfiltration, and tool/MCP abuse, with a tamper-evident audit trail.

Tagged v0.3.1 — all 12 build-order stages complete. 11-service Docker Compose stack, 53 test files, 3 CI workflows (ci, release, security). Publishes its own adaptive red-team result instead of a vendor catch-rate demo: real-model hardening cuts round-1 bypass rate (10.8% → 9.2%) but overall bypass rate under sustained adaptive attack stays flat at ~48%.

Architecture

A Go gateway fronts the defended chat pipeline: requests pass through Python input-defense detectors, a Go policy-engine evaluating CEL policy packs, and a provider-agnostic Go model-router, then Python output-defense detectors (plus an LLM judge) before any response is released. A separate agent-gate (Go) enforces tool/MCP call permissions with taint tracking. Every enforcement decision is Ed25519-signed into a Postgres-backed audit trail. A continuous red-team engine runs adaptive campaigns directly against input/output defense — deliberately bypassing policy-engine — and publishes bypass-rate evidence to a React/TS dashboard.

Key decisions

Security decisions fail closed (gateway/policy-engine/agent-gate outage returns 502, never releases an unchecked response) — only observability fails open, documented explicitly in FAILURE_MODES.md
No static default credentials anywhere in the repo — dashboard and gateway keys are generated at container startup or via a credential script
Publishes its own adaptive red-team bypass rates honestly, including the unflattering result that hardening barely moves sustained bypass rate — continuous monitoring over a one-time "solved" claim
Red-team probes input/output defense directly, deliberately bypassing policy-engine, to measure detector effectiveness in isolation
Tamper-evident audit trail via Ed25519-signed receipts, not plain logs

Go
Python
TypeScript
CEL policy-as-code
Prompt-Guard
Toxic-BERT
spaCy NER
PostgreSQL
Redis
React

System shape

View repository →

hydra-data-factory

Validated

Production-validated AV telemetry lakehouse — contract validation, DLQ isolation, Terraform-provisioned AWS path.

Production-validated pipeline with measured run results: 42,972 records ingested · 91.8% acceptance · 0% post-gate rejections.

Architecture

Ingest mock fleet JSON → Pydantic + Pandera contract validation with DLQ isolation for rejects → PyArrow/Parquet (Snappy, Hive partitioning) into S3 → Glue catalog. Dual orchestration: local Airflow + MLflow, and AWS Step Functions + Lambda. Infra fully provisioned with Terraform (S3, Glue, IAM).

Key decisions

Dual-path orchestration (Airflow local / Step Functions cloud) sharing one transformation core
Hard contract gate with DLQ isolation — rejects never contaminate the lake
Hive-partitioned Parquet for query-friendly AV telemetry
Everything provisioned as Terraform — no console-only resources

Pydantic
Pandera
PyArrow
Parquet
Terraform
S3
Glue
Airflow
Step Functions
MLflow

System shape

View repository →

Also building

Security, edge vision, geospatial.

Active side systems — lighter treatment, same bar for accurate links.

AegisFlow

Multi-agent incident response on Temporal, PydanticAI, and OpenTelemetry — triage, plan with approval, verify, compensate.

edgevision

Edge CV anomaly detection for existing camera fleets — VMS-agnostic threat analytics (YOLO + ByteTrack lineage).

smart-assistant

Personal knowledge and workflow agent for local context and task automation.

terra-OBIA

Object-based image analysis for forestry stand delineation, wetland classification, and land-cover mapping at province scale.

terra-obia-etl

ETL for GeoNB / GNB open data — harmonized labeled stand polygons feeding terra-OBIA classifiers.

Foundations

Earlier systems that shaped the craft.

Grouped briefly — perception, serving, telemetry, and API foundations.

vit-fastapi-cloud-deploy ViT classification service on FastAPI + Docker + GCP object-detection End-to-end OD train → track → serve → CI/CD multi-modal-od Radar + LiDAR + camera fusion for AV detection cuda-optimization PyTorch optimization playbook (AMP, compile, MPS) lstm-attention-transformers Sequence models, attention viz, seq2seq workflows task-master FastAPI + PostgreSQL + Docker + Actions API bert-inference Containerized DistilBERT sentiment inference NEXUS-CV Real-time multi-modal CV platform with MLOps sentinel-ray Ray ingest + Pandera QA + drift + incidents vanguard-telemetry-monitor Fleet telemetry sim, anomalies, Prometheus/Grafana

Skills & stack

Where the work actually concentrates.

Drawn from 30 public repositories spanning LLM infra, data engineering, and computer vision — organized by the systems I ship, not an exhaustive badge wall.

LLM Serving & Inference

vLLM
Triton Inference Server
TensorRT-LLM
Ray Serve
KServe
BentoML
GPTQ / AWQ / FP8
LoRA / PEFT
Bedrock
SageMaker

GPU Orchestration & Cloud

Kubernetes
Kueue
Karpenter
NVIDIA MIG
AWS
GCP
Terraform
Helm
Argo CD
Docker

MLOps & Experiment Tracking

MLflow
Weights & Biases
DVC
GitHub Actions
Dagster
Kubeflow
Prometheus
Grafana
OpenTelemetry

Computer Vision & Perception

OpenCV
ONNX
YOLO / ByteTrack
2D/3D detection & tracking
Sensor fusion (radar / LiDAR / camera)
PyTorch
Active learning / pseudo-labeling
Edge AI

Data & Streaming Systems

Kafka / Redpanda
Apache Flink
Apache Iceberg
PyArrow / Parquet
Pandera
Pydantic
Airflow
Step Functions
Ray
PySpark / Databricks
dbt

Security & Agentic Systems

Prompt-injection defense
Policy-as-code (OPA)
LangGraph
PydanticAI
Temporal
Audit trails
Red-teaming patterns

Contact

Let’s talk platforms.

Open to conversations about AI infrastructure, LLM serving, MLOps platforms, and agentic security — especially roles where production reliability matters as much as model quality.

Available globally · open to remote

hamidmatiny@gmail.com LinkedIn