# Sairam Krishnan

> I lead engineering teams at Apple as Senior Engineering Leader, Knowledge Graph Platform. At Amazon, I designed the data-aggregation and caching platform for the Alexa Engine that saved $40M a year.

I led an eight-engineer team across Seattle, London, and partner sites in Europe and Latin America. I led architecture and cross-functional delivery of Apple's media knowledge graph, the canonical model unifying 15+ sources to power search, recommendations, and discovery for over 100 million users.

## Pages

- [Home](https://sairambkrishnan.com/): Scale and results from Apple, Amazon Alexa, Praxtera, and Wharton.
- [Services](https://sairambkrishnan.com/services): Architecture Call, Design Review, Advisory Retainer, Masterclass, Keynote, Architecture Audit, AI ROI Review, Masterclass Series, Evals Harness, with starting prices.
- [Speaking](https://sairambkrishnan.com/speaking): I keynoted the Philadelphia Eagles’ Eagles Care Summit for more than 300 leaders and delivered talks, courses, and workshops for HP, Morgan Lewis, PBS, Atos, and Carnegie Mellon.
- [Career](https://sairambkrishnan.com/career): Every role and school, with the work and results in each, and recommendations from colleagues and Apple manager reviews.
- [Research](https://sairambkrishnan.com/research): My first-author papers on engineering management, AI ROI, and drone-swarm control, with DOIs.
- [Projects](https://sairambkrishnan.com/projects): Ondoway, Persuaider, Lore / DependIQ, Crucible, and the full repository archive.
- [Resume](https://sairambkrishnan.com/assets/Sairam_Krishnan_Resume.pdf): My resume as a PDF.
- [Contact](https://sairambkrishnan.com/contact): Email, LinkedIn, phone, and resume.

## Career

- Praxtera AI Institute — CTO and Curriculum Designer, AI/ML Executive Education (Sep 2025 – Present). Designed an executive AI curriculum built on original Wharton research and taught it to C-suite leaders at HP, Morgan Lewis, PBS, and the Philadelphia Eagles, helping executives evaluate, govern, and deploy AI.
- Apple — Senior Engineering Leader, Knowledge Graph Platform (Feb 2021 – Present). Led an eight-engineer team across Seattle, London, and partner sites in Europe and Latin America.
- Amazon — Senior Software Engineer, Alexa (Apr 2018 – Feb 2021). Led an eight-engineer team on the Alexa Core Engine.
- Susquehanna International Group (SIG) — Senior Financial Data Engineer (Nov 2016 – Mar 2018). Led real-time FINRA compliance surveillance across the firm's market-making transactions in C++, Python/Pandas, and Django, flagging violations at the moment of execution.
- Microsoft — Software Development Engineer, Windows OS Group (Mar 2015 – Oct 2016). Cut Windows 10 Mobile Mail power consumption 4x against the Windows Phone 8 baseline, and set the app's power targets.
- Amazon Web Services (AWS) — SDE Intern (May 2014 – Aug 2014). Unified the logging system: designed a single common schema for every team's log messages, convinced the teams to adopt it, and consolidated their custom loggers under one shared framework.
- LeanFM Technologies — Full-Stack Web Developer Intern (Dec 2013 – Feb 2014). Developed a Django admin interface for building managers.

## Education

- The Wharton School, University of Pennsylvania — Executive MBA, WEMBA Class of 2026 (May 2024 – May 2026). Earned Palmer Scholar honors (top 5% of class). Won the Benjamin Franklin Award (peer-voted; one of three recipients in a class of 250). Made the Director's List in all six terms. Earned First Year Honors.
- Carnegie Mellon University — M.S. and B.S. in Electrical & Computer Engineering (2010 – Dec 2014). Earned an M.S. in Electrical & Computer Engineering, GPA 4.00/4.00 · Dec 2014. Earned a B.S. in Electrical & Computer Engineering with a minor in Business Administration · May 2014. Made the Dean's List, Carnegie Institute of Technology · 2012, 2013, 2014.

## Research

- Who Trains the Next Senior? Managing Software Engineering When AI Makes Code Cheap. Sairam Krishnan, Gajendra Babu Thokala. SSRN, 2026. https://doi.org/10.2139/ssrn.7083078
  Generative and agentic AI have made the routine work of software engineering nearly free, and the first casualty is the junior engineer. Cheap machine-made code commoditizes breadth while raising the value of depth, the judgment of which answer is right in this system, and divergence, the perspective from outside the consensus that a senior-plus-AI team structurally lacks. Left to inertia, organizations replace juniors and let review and apprenticeship hollow out. The paper lays out the alternative: shrink the unit of review, tier scrutiny by consequence, measure verified outcomes rather than raw output, and rebuild the apprenticeship on purpose.
- A Multi-Layer Framework for Evaluating the Return on Investment of AI Projects. Sairam Krishnan, Andrea Hepp, Sonia Gandhi. SSRN, 2026. https://doi.org/10.2139/ssrn.6732598
  More than 80 percent of AI projects fail to deliver measurable returns, and traditional capital budgeting misses what sets AI apart: deeply uncertain costs, indirect and non-linear benefits, path-dependent value creation, and acute sensitivity to organizational readiness. The framework integrates four layers into one investment decision process: a SCALE strategic readiness assessment, ROIC decomposition through a value driver tree, stage-gate phased investment, and a synthesized decision scorecard. An eight-category risk-adjustment table sets the discount rate for AI-specific risks, including vendor and platform dependency and organizational friction. The framework is validated against eight deployments: Salesforce Agentforce, Palantir, Spotify, Duolingo, Shopify Magic, Uber, JPMorgan COiN, and NatWest.
- Drone-Megaddon 2026: Co-Intelligent Swarm Control with Language and Vision Models. Sairam Krishnan, Daniel Ting. engrXiv, 2026. https://doi.org/10.31224/6987
  Twelve years after the original interface, commercial drones ship with LTE and GPS-denied navigation, drone warfare has produced public lessons on electronic warfare and operator load, and foundation models have made natural-language intent and visual grounding workable interfaces to robotic systems. The paper keeps the 2014 core of RTS selection, the $RAD command queue, ACK’d radio, and human oversight, and proposes three AI layers over it: an intent translator that turns an operator’s words into a validated command sequence, a perception grounder that binds a phrase like “follow the red sedan” to a tracked target in a drone’s video feed, and a coordination planner that gives every drone its own final position. It walks through a search-and-rescue example and addresses the ethical questions the 2014 paper did not.
- Drone-Megaddon: A Real-Time Strategy Interface for Managing Drone Swarms. Sairam Krishnan, Daniel Ting. engrXiv, 2014. https://doi.org/10.31224/6924
  Researchers had controlled single drones through neural sensors, Oculus Rift, and mobile devices, but no adequate interface existed for commanding many drones at once. Drone-Megaddon is an Android real-time strategy interface patterned after StarCraft II, built end to end as a cyberphysical system: the operator selects drones, issues a high-level command, and the swarm executes it.

## Speaking

- AI for Impact — Philadelphia Eagles, Mar 18, 2026. Keynoted the Philadelphia Eagles' 12th annual Eagles Care Summit at Lincoln Financial Field on March 18, 2026, delivering "AI for Impact" to more than 300 community and business leaders.
- Executive AI Education — Praxtera AI Institute, Sep 2025 – Present. Designed an executive AI curriculum built on original Wharton research and taught it to C-suite leaders at HP, Morgan Lewis, PBS, and the Philadelphia Eagles, helping executives evaluate, govern, and deploy AI.
  - PBS, Feb 27, 2026: Designed and taught an AI strategy session for PBS.
  - Morgan Lewis, Jan 20, 2026: Designed and taught an AI adoption and change-management workshop for the firm’s trainers and champions.
  - HP, Oct – Nov 2025: Designed and taught a two-session course at HP.
- Knowledge Graphs, Then Small Language Models — Atos · Independent, Sep 3, 2026. Presented what a knowledge graph is, how it enriches questions before they reach a model, and how it checks the model’s answer afterward, then where graphs go next: sub-graphs by topic, failure graphs, codified expertise, and the graph behind an API. Covered small language models next: what they are, when to go small and when to go big, a live setup of a local model in five minutes, and how they change the way people work. Deck: https://sairambkrishnan.com/talks/knowledge-graphs
- Web Applications — Carnegie Mellon University, May 2013 – Dec 2013. Redesigned the Web Applications course with the professor, planning a 20-lecture semester from client-side code and HTTP through relational and NoSQL databases, servlets, security, and Django. Built the client-side and server-side exercise sets and reference survey apps in Spring MVC, Play, and Django, and instructed 105+ students as Agile Product Owner for their final projects.

## Services

- Architecture Call (60 minutes), from $500: One hour on the design, scaling problem, data model, or AI build your team is stuck on. Bring the doc or the diagram; leave with a recommendation.
- Design Review (1 week), from $2,500: A written review of one design doc before your team commits to building it: data models, service boundaries, failure modes, and scale. Every risk is ranked with a proposed fix, and the week closes with a call to walk your engineers through it.
- Advisory Retainer (About 10 hours a month), from, a month $5,000: Standing access for your engineering leaders: design reviews before work starts, office hours for hard distributed-systems and AI problems, and mentoring for senior engineers growing into staff roles.
- Masterclass (2–4 hours), from $10,000: One session from the catalog below, fitted to your firm, for your executives or engineers. I taught AI strategy to C-suite leaders at HP, Morgan Lewis, PBS, and the Philadelphia Eagles.
- Keynote (One talk), from, plus travel $10,000: A keynote on any topic in the catalog below, for a conference, summit, or company offsite.
- Architecture Audit (2–3 weeks), from $15,000: A review of a whole system, not one doc: every service boundary, data contract, and dependency, and the bottlenecks, failure loops, and batch jobs that should be streams. Ends with a ranked remediation plan and a working session with your senior engineers.
- AI ROI Review (2–3 weeks), from $15,000: I run every AI initiative on your list through the four-layer framework from my SSRN paper: a SCALE strategic readiness assessment of your firm, a value driver tree that traces each initiative to return on invested capital, a discount rate adjusted for AI-specific risks such as vendor and platform dependency and organizational friction, and stage gates that release each tranche of funding only on evidence. You get a decision scorecard that tells your executives which initiatives to fund.
- Masterclass Series (Two sessions), from $15,000: Two sessions from the catalog below, built as a sequence, like the two-part course I designed and taught at HP.
- Evals Harness (3–4 weeks), from $20,000: A test suite that shows whether a prompt, model, or pipeline change made your AI better or worse: deterministic checks first, a golden set of real queries with expected answers, and model-graded judges where code cannot check. It runs on every code change, so no change ships without passing.
- Masterclass and keynote topics: AI Foundations for Digital Innovators; Applying AI in IT and Digital Solutions; AI Adoption and Change Management; AI Strategy, Governance First; AI for Impact; Knowledge Graphs, Then Small Language Models; Which AI Projects Will Pay Off?; Who Trains the Next Senior?. Remote by default. On-site sessions add travel.

## Projects

- Ondoway: Walking tours rarely know where a listener is, what they care about, or which story belongs to the exact corner in front of them. Designed, architected, coded, and operated end to end. Built with a provenance-aware Neo4j knowledge graph, deterministic tour engine, FastAPI backend, authoring tools, and Flutter mobile app.
- Persuaider: Negotiation training is difficult to personalize, repeat, and evaluate consistently. Built the complete product and its multi-provider AI operating model. Built with scenario authoring, in-character AI personas, framework-based evaluation, security controls, and transparent model failover.
- Lore / DependIQ: Repository-local tooling cannot explain the organization-wide blast radius of a dependency change. Designed the product, graph model, algorithms, and senior-engineer audit. Built with PostgreSQL for identity and history, Neo4j for dependency traversal, and task-aware model routing across hosted and local models.
- Crucible: Prompt-only guidance is too easy for coding agents to ignore when delivery pressure rises. Encoded engineering practice into skills, agents, hooks, and verifiable workflows. Built with ten skills, ten agents, four patterns, deterministic gates, and adversarial evidence checks.

## Profiles

- LinkedIn: https://www.linkedin.com/in/sairambkrishnan
- GitHub: https://github.com/SaiWebApps
- ORCID: https://orcid.org/0009-0004-2626-1273
- Google Scholar: https://scholar.google.com/citations?user=iQAZ87gAAAAJ
- Resume: https://sairambkrishnan.com/assets/Sairam_Krishnan_Resume.pdf

## Contact

sairambkrishnan@gmail.com