Matiwos Desalegn

• Built an enterprise multi-agent RAG document assistant - LangChain + CrewAI over Llama 3.3 70B (Groq), ChromaDB hybrid retrieval with cross-encoder reranking, Keycloak SSO, and PostgreSQL conversation memory
• Built the end-to-end credit-scoring ML pipeline - Feast feature store (S3 registry, Redis online, Aurora offline) with automated feature-refresh across batch domains; FastAPI inference on EKS with <200ms p95 latency
• Built an AI data-standardization agent using a provider-agnostic LLM layer (OpenAI / Ollama / HuggingFace), containerized and deployed on AWS
• Developed a FastAPI embedding service for Ethiopian-language retrieval (EthioLLM; Amharic/Ge'ez/Afaan Oromo/Tigrinya/Somali) with batch processing and a fallback model

• Built the organization's first AWS-managed lakehouse (Athena + Glue Data Catalog) orchestrated with Mage AI - 6-hour micro-batch ingestion for institutional partners, 300+ tables / 7,000+ columns, high-watermark extraction, and date partitioning
• Designed a lightweight real-time CDC pipeline under compute constraints - OLake for MySQL binlog to Apache Iceberg (Polaris catalog), queryable via Trino
• Built and deployed the Batch API microservice (FastAPI + EKS) ingesting domain payloads from partner institutions; persists to MongoDB and pushes features to Feast (Redis) for credit-scoring consumption
• Built production Firebase Analytics export pipelines - BigQuery to S3 Bronze Parquet to Iceberg Silver via PyIceberg, with partner-isolated Airflow DAGs and Secrets Manager + IRSA

• Designed and implemented the frontend of an enterprise HR/payroll (ERP) system, improving usability and operational efficiency
• Integrated RESTful APIs with backend teams for reliable, end-to-end data flow
• Achieved a 40% reduction in payroll processing time through optimized data handling and a responsive interface

• Intensive 12-week data science and machine learning fellowship program
• Worked on real-world data science projects with industry mentors
• Specialized in MLOps, feature engineering, and model deployment
• Collaborated in agile teams on end-to-end ML projects

• Led a community of 200+ developer students in learning cutting-edge technologies
• Organized workshops and events on cloud computing, AI/ML, and web development
• Mentored students in building real-world projects and technical skills
• Facilitated hackathons and coding competitions

End-to-end analytics engineering pipeline with PostgreSQL OLTP, Debezium CDC, Redpanda, ClickHouse OLAP, dbt marts, Airflow orchestration, and Grafana observability plus a live demo environment.

~3K rows ingested from paginated REST API · CDC replication lag monitored with cdc-monitor alerts

Mage AI-orchestrated ETL system ingesting loan data from 5 Ethiopian partner bank Metabase instances every 6 hours into AWS S3, with PAR (Portfolio at Risk) transformation and Athena-queryable cleansed layers for digital lending analytics

6-hour ingestion cadence across 5 partner banks · Unified RAW to Application to Cleansed S3 layer structure

Automated daily pipeline exporting 600K–800K Firebase Analytics events from Google BigQuery to AWS S3 in Parquet format with Snappy compression

600K–800K events exported daily · ~70% storage reduction via Parquet + Snappy

Production FastAPI microservice for batch ingestion of 18+ financial and behavioral data domains, powering credit scoring for 5 Ethiopian partner banks via Feast Feature Store and ML pipelines

5–10 pod autoscaling under load · 99.9% pipeline reliability

Production multi-source data platform ingesting core banking, sharia-compliant, operational, and portfolio risk data from 5+ partner banks into Aurora PostgreSQL and Amazon Redshift, powering credit risk, portfolio analytics, and Power BI dashboards for a digital lending business

5 source systems, daily full-refresh ingestion · Zero-downtime atomic table swap for all partner banks

End-to-end dimensional modeling project for a digital lending platform - from conceptual and logical design through physical Star Schema implementation (fact and dimension tables) in Amazon Redshift - covering Transient Staging, Core Canonical Data Model, and a Semantic Layer with Data Contracts for analytics, credit risk, and BI consumption

99.9% uptime - Kubernetes (EKS) containerization · CI/CD via GitHub Actions + GitLab CI/CD; GDPR-compliant anonymization

10 Academy · Remote

Intensive 12-week program — graduated top 10% of cohort. Specialized in MLOps, feature engineering, and end-to-end ML project deployment.

Wolaita Sodo University · Wolaita Sodo, Ethiopia

Grew community from 50 to 200+ members; organized 12 workshops and 3 hackathons with 100+ participants each.