Lokesh LKS — AI Infrastructure, Security & Quant-leaning Engineer

I design and build systems at the intersection of AI infrastructure, distributed systems, and security, with a secondary interest in quantitative finance.

Right now I am:

• Executing a focused roadmap in PyTorch, distributed systems, and ML system design to move into AI infrastructure / ML platform roles:contentReference[oaicite:0]{index=0}
• Sharpening C++ / Python and data structures & algorithms daily
• Exploring low-latency systems and trading-style architectures as a backup path into quant

What I’m looking for

• Roles: AI Infrastructure / ML Systems Engineer, Platform ML Engineer, Security-focused ML Engineer
• Secondary path: Quantitative developer / research engineer roles with strong systems + math focus
• Timeline: Internships and early-career roles aligned with a 2026 start

If you’re hiring for teams working on model training, serving, data/feature platforms, or security for ML systems, I’d like to talk.

Flagship Projects

Custom Static Site Generator (this site)

A C++17 static site generator that converts Markdown → HTML and auto-builds this portfolio.

• C++17, STL, \, regex, CLI tooling
• Markdown parser, HTML templating, auto-generated navigation
• GitHub Actions CI for auto-deploy to GitHub Pages

See details on the Projects page

ThreatLens (AI for Security)

End-to-end intrusion / anomaly detection pipeline:

• Python, PyTorch, NumPy, Pandas
• Experimented with LSTM / CNN-style models on network-style data
• Focus on throughput, latency, and deployment rather than just accuracy

AI Infrastructure Roadmap

A structured 4–6 month plan to grow from cybersecurity + systems to AI infra engineer, covering:

• PyTorch + ML fundamentals
• Distributed systems (DDIA, MIT 6.824)
• ML system design & MLOps (MLflow, Ray, Kubernetes basics)
• Interview-focused DSA and system design practice

Skills Snapshot

Languages

• C++, Python, SQL, basic Bash

Systems & Infra

• Linux, networking basics, Docker
• Distributed systems fundamentals (replication, partitioning, CAP trade-offs)

ML & Data

• PyTorch, NumPy, Pandas
• ML system design (training → serving → monitoring)
• Experiment tracking, reproducible pipelines

Security

• Cybersecurity foundations (network security mindset)
• Threat/anomaly detection framing, adversarial thinking

Why this profile is a fit

• Systems + security DNA: I naturally think about failure modes, adversaries, and robustness, which maps well to production ML systems and fraud/security use cases.
• AI infra trajectory: My plan is explicitly optimized for AI infra / ML platform roles, not generic “ML engineer” work.
• Quant-leaning: Strong interest in low-latency, data-heavy systems, relevant to trading, market data infrastructure, and risk systems.

If this aligns with your team’s work, reach out — I’m building for exactly this niche.