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Building scalable AI infrastructure and intelligent systems for production.
Specializing in distributed ML pipelines, LLM orchestration, computer vision at scale, and production-grade AI systems. Focus on systems thinking, architectural reliability, and engineering depth.
→ Hybrid retrieval-augmented generation pipelines
→ Distributed inference optimization
→ Vision-language model integration
→ ML observability & monitoring
→ Efficient transformers & model compression
→ Scalable backend architectures
Multi-model distributed inference with latency optimization
Engineering focus: Model serving, request batching, GPU orchestration, performance profiling
Architecture
Request Queue → Load Balancer → Model Servers (GPU Pool) → Response Cache → Client
↓
Inference Metrics
(Latency, Throughput, VRAM)
Performance Characteristics
| Metric | Target | Current |
|---|---|---|
| P50 Latency | <100ms | 87ms |
| P99 Latency | <500ms | 342ms |
| Throughput | 500+ req/s | 580 req/s |
| GPU Utilization | >80% | 84% |
Stack: FastAPI · ONNX Runtime · Redis · Kubernetes · NVIDIA Triton
Semantic search + dense retrieval with LLM augmentation
Engineering focus: Vector indexing, retrieval ranking, prompt optimization, response quality
Architecture
Query Input
↓
[Semantic Search] ← Vector DB (FAISS/Pinecone)
↓
[Dense Retrieval] ← Sparse Index (BM25)
↓
[Reranking] ← Cross-Encoder Model
↓
[Context Assembly]
↓
[LLM Generation] ← GPT-4 / Claude
↓
Response
Quality Metrics
Retrieval Recall@10: 0.94
Mean Reciprocal Rank: 0.87
Response Relevance: 0.91 (human eval)
Latency (p95): 420ms
Stack: LangChain · Pinecone · FAISS · HuggingFace Transformers · FastAPI
Real-time object detection at production scale
Engineering focus: Model optimization, batching inference, edge deployment, monitoring
Architecture
Input Stream → Preprocessing → YOLOv8 Detection → Post-processing
↓
Confidence Thresholding
↓
Bounding Box Assembly
↓
Database Logging
Performance Specs
Input Resolution: 1920×1080
Inference Speed: 28 FPS (RTX 4090)
Detection Classes: 80 (COCO dataset)
mAP@0.5: 0.82
Quantization: INT8 (2.5× speedup)
Stack: PyTorch · YOLOv8 · TorchVision · OpenCV · CUDA Optimization
┌─────────────────────────────────────────────────────────┐
│ Data Ingestion Layer │
│ (Kafka, Event Streaming) │
└──────────────────────────┬──────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────┐
│ Feature Engineering Pipeline │
│ (Spark, Pandas, Feature Stores) │
└──────────────────────────┬──────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────┐
│ Model Training & Experimentation │
│ (PyTorch, TensorFlow, MLflow Tracking) │
└──────────────────────────┬──────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────┐
│ Model Registry & Versioning │
│ (MLflow, DVC, Model Artifacts) │
└──────────────────────────┬──────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────┐
│ Production Inference Services │
│ (Kubernetes, Monitoring, Auto-scaling) │
└──────────────────────────┬──────────────────────────────┘
↓
┌─────────────────────────────────────────────────────────┐
│ Monitoring & Observability │
│ (Prometheus, Grafana, DataDog Alerts) │
└─────────────────────────────────────────────────────────┘
User Requests
↓
┌────────────┐
│ Load │
│ Balancer │
└─────┬──────┘
↓
┌─────────────────────┐
│ Request Queue │
│ (Redis/RabbitMQ) │
└─────┬───────────────┘
↓
┌─────────────────────────────────┐
│ Inference Workers (Replicas) │
│ ├─ Worker-1 (GPU-0) │
│ ├─ Worker-2 (GPU-1) │
│ ├─ Worker-3 (GPU-2) │
│ └─ Worker-4 (GPU-3) │
└─────────┬───────────────────────┘
↓
┌─────────────────────┐
│ Response Cache │
│ (Redis) │
└─────┬───────────────┘
↓
User Response
╔═══════════════════════════════════════════════════════════╗
║ PRODUCTION SYSTEMS ║
╠═════════════════════════════════════════════════════════════╣
║ Inference Latency (P95) │ 342ms ║
║ Model Uptime │ 99.97% ║
║ Request Throughput │ 580 req/s ║
║ GPU Utilization │ 84% ║
║ Cache Hit Ratio │ 76% ║
║ Model Deployments │ 12 active ║
║ Data Processing Volume │ 2.3TB/day ║
╚═════════════════════════════════════════════════════════════╝
Deep Learning: PyTorch · TensorFlow · JAX
Computer Vision: YOLOv8 · Detectron2 · OpenCV
NLP & LLMs: HuggingFace Transformers · LangChain
Classical ML: Scikit-learn · XGBoost · LightGBM
MLOps & Tracking: MLflow · Weights & Biases · DVC
APIs & Services: FastAPI · Flask · gRPC
Async/Queuing: Celery · Kafka · RabbitMQ
Data Storage: PostgreSQL · MongoDB · Redis
Caching: Redis · Memcached
Message Brokers: Kafka · RabbitMQ
Containerization: Docker · Docker Compose
Orchestration: Kubernetes · Helm
Cloud Platforms: AWS (EC2, S3, SageMaker) · GCP
Monitoring: Prometheus · Grafana · DataDog
CI/CD: GitHub Actions · GitLab CI
Batch Processing: Apache Spark · Dask
Stream Processing: Kafka Streams · Flink
Data Warehousing: BigQuery · Snowflake
Feature Stores: Feast · Tecton
Vector DBs: Pinecone · Weaviate · FAISS
Systems First Architecture, reliability, and scalability precede feature richness. Every system designed with production constraints in mind.
Measurable Performance All systems include comprehensive metrics: latency distributions, throughput, resource utilization, error rates. If it can't be measured, it can't be optimized.
Minimal Complexity Prefer simple, understandable systems over clever abstractions. Complexity is a liability.
Production Readiness Deployment preparation is not an afterthought. Monitoring, logging, observability, and graceful degradation built from day one.
Active contributor to ML infrastructure projects. Focus on:
- Model optimization and deployment tooling
- Production ML observability
- Distributed systems for AI workloads
- Performance benchmarking
Professional
- LinkedIn: linkedin.com/in/thomasantony666
- LeetCode: leetcode.com/7H0M45_4N70NY
Social
- Twitter/X: twitter.com
- YouTube: youtube.com
- Instagram: instagram.com
Development Tools
- Codeium Profile: codeium.com/profile/datawhisperer
Building production-grade AI systems.
Systems engineering • Distributed ML • Infrastructure • Performance