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Alaisha Alexander
Experience

A through-line, not a résumé

Each role taught me something specific about building systems people can trust. Here's the work, the impact, and what it left me with.

  1. Senior AI Engineer

    Current

    Aimpoint Digital · Atlanta, GA · Jul 2022 – Present

    Design and deliver data and AI systems for enterprise clients, and build the internal frameworks, tooling, and education that let teams deliver them repeatably.

    What I did

    • Lead the design and implementation of data and AI solutions for clients across financial services, manufacturing, and restaurant industries, working directly with client leadership and cross-functional internal teams.
    • Author the internal documentation and playbooks for building, deploying, and governing Snowflake Native Applications — turning one-off delivery into a repeatable practice.
    • Build automated intake and architecture tooling that standardizes solution-design decisions across 120+ criteria, so teams reach consistent, defensible architectures faster.
    • Design and teach enablement — onboarding materials, internal notebooks, and hands-on training across Snowflake and Google BigQuery — for both colleagues and clients.

    Impact

    • Led development of diagnostic tooling that reduced Snowflake credit spend by roughly 80% across multiple enterprise clients.
    • Built and taught an AI-powered Snowflake application for non-technical users, translating complex AI concepts into workflows they could own.
    • Raised baseline technical fluency company-wide through blogs, tutorials, and notebooks on Snowflake AI/ML capabilities.
    SnowflakeSnowflake Native AppsGoogle BigQuerydbtPythonSQLCortex / LLMsDataiku

    The hardest part of enterprise AI is rarely the model. It's deciding what should exist, getting an organization aligned on it, and making the result something people will still use six months later. Good architecture and good enablement are two halves of the same job.

  2. Instructor & Mentor

    Current

    Inspirit AI · Palo Alto, CA (remote) · Jun 2023 – Present

    Design and teach multi-week AI curricula, translating advanced concepts into approachable, hands-on learning for students from a wide range of backgrounds.

    What I did

    • Designed and delivered curricula spanning machine learning, NLP, and applied AI to diverse student cohorts.
    • Translated advanced AI concepts into practical projects for learners without a technical background.
    • Mentored students through building and reasoning about their own models.

    Impact

    • Helped students move from 'I don't understand any of this' to shipping a working AI project they can explain.
    PythonMachine LearningNLPApplied AI

    Teaching is the fastest way to find the gaps in your own understanding. If you can't make a concept land for a bright fifteen-year-old, you probably don't understand it as well as you thought.

  3. NSF Graduate Research Fellow

    Stanford University · Stanford, CA · Sep 2018 – Dec 2021

    Designed perception-to-control systems for autonomous decision-making under uncertainty — the origin of how I think about AI systems today.

    What I did

    • Designed and optimized end-to-end perception-to-control systems for autonomous decision-making under uncertainty, balancing multiple predictive models against real-time constraints.
    • Developed and evaluated optimization and control frameworks — model predictive control, probabilistic cost functions — and reduced computational expense by ~50% while holding performance.
    • Built policy-based decision systems using reinforcement learning and Markov Decision Processes to improve adaptive behavior in dynamic, multi-agent environments.
    • Led development of multi-agent coordination frameworks enabling synchronized decision-making across distributed systems.

    Impact

    • Cut computational cost of control frameworks by roughly half without sacrificing system performance.
    • Advanced multi-agent coordination methods for decision-making under real-world uncertainty.
    Model Predictive ControlReinforcement LearningMDPsOptimizationPythonC++JuliaROS

    Every real system is a negotiation between what's optimal and what's affordable in time and compute. I stopped chasing the best possible answer and started designing for the best answer you can actually compute and trust in the moment you need it.

  4. National Treasurer → Treasurer Emeritus

    National Society of Black Engineers · Alexandria, VA · May 2018 – May 2022

    Owned financial strategy and governance for a national nonprofit with a $12–15M annual budget — leadership at organizational scale, in parallel with graduate research.

    What I did

    • Owned financial strategy, governance, and operational oversight across programs, conferences, staff operations, and long-term reserves.
    • Led audit, compliance, and financial reporting in partnership with staff experts, ensuring fiscal health across multiple fiscal years.
    • Presented quarterly performance, forecasts, and strategic recommendations to the National Executive Board and major corporate sponsors.
    • Coordinated a cross-functional team of 10+ staff and volunteer leaders.

    Impact

    • Designed and executed policy changes that reversed multi-year operating deficits and stabilized long-term earnings.
    • Spearheaded a new auditing Standard Operating Procedure to improve transparency, accountability, and repeatability.
    Financial StrategyGovernanceAudit & ComplianceNonprofit Operations

    Leading a $15M budget while doing research taught me that strategy is mostly about making the right decisions legible to people who have to trust them. The SOP mattered as much as the numbers — repeatability is a form of leadership.