Principal SDE @ Infrrd

Anuj Sadani

People · Business · Technology

I build AI systems — and the teams that build them. Turning GenAI enthusiasm into software that survives contact with real users.

Anuj Sadani
Philosophy

People > Business > Technology

Not a ranking of what is interesting — an order of operations. I have yet to see a hard engineering problem that was not, one layer down, a question about this order.

01

People

People come first because they are the only part that compounds. Systems decay, frameworks get replaced, and the roadmap you defend today will be rewritten. What remains is the judgement in the room — and judgement is grown, not hired in finished form.

So I treat mentoring as the actual work, not the overhead around it. If the team ends a year abler than it started, the systems tend to follow.

In practice mentoring 12+ Python and ML engineers, hiring for density over headcount.

02

Business

Business sits in the middle because it is the honest translator. It is where an idea has to survive a question engineers would rather skip: what does this change for someone who does not care how it works?

Engineering that cannot answer that gets decided for it, by people further from the code. I would rather make the argument myself, with a number attached.

In practice turning requirements into technical OKRs, and hype into bets with a cost attached.

03

Technology

Technology comes last, and I say that as the person who enjoys it most. It is the layer with the best feedback loops and the least ambiguity, which is exactly why it is so tempting to start there — picking the stack is easier than admitting you have not agreed on the problem.

Put last, it becomes leverage. Put first, it becomes a very expensive opinion.

In practice enterprise RAG, agent tooling on MCP, fine-tuned open models, model routing, evals, and the org-wide Python stack.

On imbalance

The order is useful mainly because of what it catches when it slips. Technology chosen first, the business case retrofitted around it, and people quietly absorbing the difference — that pattern has a familiar ending, and it is never fixed by a better framework.

An imbalance here is a signal, not a style. It asks to be corrected before anything downstream of it will hold.

Journey

Three very different rooms

Silicon, then logistics, then enterprise AI. Tap any role to see what happened there.

  • The scale it runs at: the document extraction platform peaks around 1M pages a day, held to 1–4¢ a page depending on what the document is.
  • Recognition that stuck: architected the core GenAI and ML capabilities behind Infrrd's placement as a Leader in the Gartner Magic Quadrant, plus Everest Group recognition.
  • Innovation streak: coordinated cross-departmental R&D that earned the company Deep Analysis' "Innovator of the Year" run.
  • Python, org-wide: own and evolve the entire Python stack across teams — enterprise standards for security, async performance (FastAPI/Flask), and code quality.
  • Cheaper than the obvious answer: a hybrid ML+AI system delivering 30–40% lower operating cost than an LLM-only implementation, via intelligent model routing and tuning.
  • Agents that do real work: built the MCP tooling layer for mortgage documents — sat with underwriting SMEs, turned their domain rules into tools the agents call in production.
  • Small models, tuned: fine-tuned small open models for entity extraction on unstructured documents, in the production path rather than in a notebook.
  • Production reality: lead Docker and Kubernetes/EKS strategy on AWS, resilient against rate limits and SLA degradation rather than merely working on a laptop.
  • Team: lead and mentor 12+ Python/ML engineers on 90-day growth plans.
  • Designed and scaled Python/Flask backend microservices for a high-traffic logistics platform, handling real-time order processing across distributed systems.
  • Worked with Product Owners and engineering leadership to turn business objectives into technical OKRs and delivery roadmaps.
  • Partnered with Data Science and BI to land analytical workflows inside production services.
  • Drove incident management and reliability through IaC (Terraform, Helm) and observability (Grafana, New Relic).
  • Led 8+ engineers building embedded automation frameworks and internal web tooling across multiple product lines.
  • Owned the full SDLC, from requirements and system design through deployment and long-term maintenance.
  • Built scalable data management for production systems with an eye on integrity and traceability.
  • Drove RCCA on complex production issues, improving triage accuracy and cutting repeat failures.
  • Pushed automation-first practice and crowdsourced internal tooling to lift engineering productivity.
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