Machine Learning Infrastructure · Distributed Systems · Data Engineering · Algorithmic Engineering
I design and build intelligent systems where machine learning, distributed computing, algorithms, and production infrastructure converge.
My focus is not isolated model development. I engineer complete systems that must operate under real-world constraints involving scale, latency, reliability, observability, security, and cost.
- Production ML pipelines and MLOps
- Model training, evaluation, deployment, and monitoring
- Distributed inference and model serving
- Feature engineering and feature platforms
- Model lineage, reproducibility, and governance
- Drift detection and continuous evaluation
- ML performance and infrastructure optimization
- Batch and streaming architectures
- Event-driven systems
- Kafka / Flink / Spark
- Data pipelines and data contracts
- Stateful stream processing
- CDC and event-driven architectures
- Fault tolerance and idempotent processing
- Distributed systems and concurrency
- Graph algorithms
- Dynamic programming
- Optimization
- Numerical methods
- Complexity analysis
- High-performance computing
- Performance engineering
- Algorithmic problem solving
- Time-series forecasting
- Anomaly detection
- Predictive systems
- Optimization
- Decision intelligence
- Reinforcement learning
- Autonomous and multi-agent systems
Languages
Python · Go · SQL
Machine Learning
PyTorch · TensorFlow · scikit-learn · Ray · MLflow · ONNX
Data Engineering
Apache Kafka · Apache Flink · Apache Spark · Airflow · dbt · Delta Lake · Snowflake
Infrastructure
Docker · Kubernetes · Terraform · CI/CD · Prometheus · Grafana · OpenTelemetry
Systems
PostgreSQL · Redis · gRPC · Protobuf · REST · Linux
Cloud
AWS · GCP · Azure
A production ML model is only one component of a larger system.
Data
↓
Ingestion
↓
Processing
↓
Features
↓
ML / Algorithms
↓
Inference
↓
Decision
↓
Monitoring
↓
Optimization