Krishna Mankali

Platform, DevOps, AI Automation, and Backend Engineer

I build production-shaped systems that connect automation, cloud infrastructure, APIs, operational intelligence, and reliable software delivery. My portfolio focuses on measurable outcomes: repeatable workflows, safer deployments, observable services, tested APIs, and maintainable delivery practices.

Featured Engineering Work

Project	Engineering focus	Evidence
AI Delivery Enablement Control Plane	Governed workflow automation, operational KPIs, cost and capacity reporting	Production release, 92%+ coverage, CI, CodeQL, multi-architecture container
Zero Outage DevOps	Secret validation, deployment gates, Terraform, Docker, AWS delivery patterns	Tested Node 24 container, successful CI, public release
RL Gym FastAPI Platform	Typed APIs for RL simulation, benchmarking, and policy evaluation	Seven tests, linting, CI, AMD64/ARM64 container
Agent Conversation Data Studio	Tool-calling conversation generation and validation	Python, JavaScript, Java, CI, public API container
LLM Code Evaluation Lab	Multi-language review of AI-generated code, tests, and fixes	Python, JavaScript, Java, Go, Rust, C++, verified CI
AWS DevOps Platform	Containerized health service, Terraform scaffolding, AWS delivery reference	CI validation and public multi-architecture container

Core Capabilities

Platform & DevOps     Docker · GitHub Actions · Terraform · AWS · Kubernetes patterns
Backend Engineering  Python · FastAPI · REST APIs · SQLAlchemy · Node.js
AI Engineering       LLM evaluation · Tool calling · Synthetic data · RL workflows
Operational Systems  Automation governance · KPIs · Reliability · Runbooks · Cost visibility
Quality & Security   Pytest · Ruff · CodeQL · CI gates · SBOM · Provenance attestations

Published Software

• Versioned GitHub Releases across automation, cloud, backend, AI, analytics, and RevOps work.
• Public GitHub Container Registry images for tested runnable services.
• AMD64 and ARM64 images with SBOM and build-provenance attestations.
• Explicit open-source licenses and client-ready setup documentation.

Explore:

• All repositories
• Published packages
• AI Delivery Enablement release
• LLM Evaluation release

Engineering Approach

1. Define the operational problem and measurable target.
2. Build the smallest repeatable service or workflow that proves value.
3. Add tests, security checks, observability, documentation, and rollback paths.
4. Package the result so another engineer can run and evaluate it consistently.
5. Separate portfolio demonstrations from claims of real client production usage.

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Based in India. Open to platform engineering, DevOps, backend, cloud automation, and AI enablement opportunities.