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Parmodk2310/README.md

Parmod

AI/ML Engineer

I build reliable AI products and ML platforms that move beyond notebooks into evaluated, observable, and deployable software.

My work spans applied AI, ML governance, product analytics, and distributed systems—with an emphasis on correctness, operational evidence, and clear engineering trade-offs.

Portfolio · LinkedIn · Technical Writing

Engineering Approach

  • Evidence over claims — tests, evaluation, observability, and reproducible releases.
  • Failure-aware design — drift controls, degraded dependencies, rollback, and recovery.
  • End-to-end delivery — from models and data pipelines to APIs, products, and cloud operations.

Explore the pinned repositories below for architecture, source code, verification evidence, releases, and live demonstrations.

For detailed case studies and my professional background, visit my portfolio.

Pinned Loading

  1. Advanced-Distributed-System Advanced-Distributed-System Public

    Correctness-first distributed runtime for reliable AI/ML services with causal CRDTs, durable recovery, mTLS, observability, chaos testing, Kubernetes, Terraform, and verified AWS delivery.

    Python

  2. CreditScorev4-ML-Governance CreditScorev4-ML-Governance Public

    Evidence-backed ML governance for data quality, drift, fairness, model integrity, staged promotion, rollback, monitoring, and incident traceability.

    Python

  3. Retention-Analytics-Platform Retention-Analytics-Platform Public

    End-to-end product analytics and ML platform for retention, churn intelligence, experimentation, and reliable event processing with FastAPI, React, PostgreSQL, Redis Streams, and GCP.

    Python

  4. AXIOM-Portfolio-Intelligence AXIOM-Portfolio-Intelligence Public

    Production-oriented portfolio research platform combining constrained optimization, risk analytics, FinBERT, FAISS RAG, grounded AI commentary, and secure AWS delivery.

    Python