Open to full-time roles from January 2027

Mayank Tamakuwala

Software Engineer · AI/ML

Software engineer building scalable systems brick by brick, with AI/ML at the core.

SDE Intern (ML), 2026
Amazon
M.S. CS (AI), Northeastern
3.95 GPA
Published, WF-PST 2026
IEEE
production apps shipped
20+
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Experience

  1. Software Development Intern (ML)

    Amazon

    May 2026 – Jul 2026Irvine, CA

    tenant onboarding, down from hours
    ~5 min
    agent-callable MCP tools
    11
    AI services behind one API
    3
    • Java
    • AWS CDK
    • DynamoDB
    • KMS
    • IAM
    • MCP
    • Lambda
    • API Gateway
    • CloudWatch
    What I did

    My team ran three internal AI services, and getting another Amazon team onto them cost hours of engineering time per tenant. I built the platform that removed that cost, starting from an empty repository: Java, provisioned with AWS CDK, DynamoDB for state, and one API in front of all three services. Onboarding now takes about five minutes and no engineer at all.

    Because tenants share the platform, authorization was the part that had to be right. KMS, SigV4 cross-account authentication, and IAM enforce isolation across AWS account boundaries.

    Using the platform still meant knowing the platform, so I designed an MCP layer over the API exposing 11 agent-callable tools. An LLM agent can now complete a multi-step workflow across all three services from a plain-language request, instead of someone walking a runbook.

    Partway through, we moved off Bedrock AgentCore onto CORAL Lambda, API Gateway, and MCP Gateway. I evaluated that trade-off and led the migration, and the observability shipped with it rather than after it: CloudWatch dashboards, per-API alarms, and SNS alerting were live on the new path from its first day serving traffic.

  2. Research Assistant (AR/VR + Machine Learning)

    Reality Design Labs, Northeastern University

    Jan 2026 – PresentBoston, MA

    average cross-device annotation
    1.09 s
    faster encode-and-upload
    8.1×
    field trials under 1.4 s
    29/29
    • PyTorch
    • Transformers
    • HoloLens 2
    • Profiling
    • Computer Vision
    What I did

    Reality Design Labs builds augmented reality for public safety. The problem I work on is deceptively hard: when one responder marks a point in their view, every teammate needs to see that same point in their own headset, outdoors, on moving cameras, with no shared clock between the devices.

    I built the cross-device tracking that makes that work. A 2D annotation from one responder resolves into a teammate's camera frame in 1.09 seconds on average, and stayed under 1.4 seconds in all 29 trials of an outdoor field session across three headsets. The perception side fuses three HoloLens 2 streams in a single joint transformer pass, replacing pairwise reconstruction that needed 7 to 10 seconds of alignment per scene.

    Most of the speed came from measurement rather than modeling. I instrumented the PyTorch pipeline end to end across client and server and profiled it stage by stage, which located encode-and-upload as the real cost and cut it 8.1x, from 7.1 seconds to 0.9 seconds per capture. Cross-device tracks stayed valid 76 to 83% of the time throughout.

  3. Full Stack Developer

    Grossi Consulting LLC

    Feb 2024 – Dec 2024Raleigh, NC

    apps shipped in 11 months
    20+
    client satisfaction
    +30%
    user engagement
    +20%
    • React
    • Next.js
    • TypeScript
    • Node.js
    • Express.js
    • Flask
    • GraphQL
    • AWS
    • GCP
    What I did

    A web design agency where no two clients got the same build. I owned projects end to end, from the first design conversation through whatever broke in production three weeks later.

    I shipped more than 20 customer-facing applications in eleven months on React, Next.js, TypeScript, and Node.js, work that contributed to a 30% increase in client satisfaction. Rebuilding from scratch every time was the obvious bottleneck. I pulled the repeating pieces into shared components on both sides of the stack, React on the front with Express.js and Flask serving REST and GraphQL behind it, which is what let a small team keep 20+ client systems maintainable at once.

    Jest covered the units, GitHub handled versioning, and Cloudflare, S3, and Elastic Load Balancing sat in front of production across AWS and GCP.

    I also instrumented what we shipped. Google Analytics, Tag Manager, and Search Console showed where users actually stalled, and changing the UX against that data rather than against opinion contributed to a 20% increase in engagement.

  4. Teaching Assistant, IE7374 / DADS 7305 Machine Learning Operations

    Northeastern University

    Sep 2026 – PresentBoston, MA

    • CI/CD
    • Model Versioning
    • Experiment Tracking
    • Data Pipelines
    • Data Drift
    What I did

    Supporting students in building and evaluating production ML systems, covering CI/CD for ML, model versioning, experiment tracking, and deployment pipelines. Developed AI-assisted grading tooling and real-time assessment aids to streamline course operations.

    Mentoring students through their semester projects, across data collection, data pipelines, CI/CD/CT/CM, model versioning, and data drift.

  5. Teaching Assistant, CS7180 AI-Assisted Coding

    Northeastern University

    Jan 2026 – Apr 2026Boston, MA

    • LLMs
    • Code Review
    What I did

    CS7180 is about how LLMs change the practice of writing software, not just its output. The subject matter moves faster than any syllabus can, which makes the course partly an experiment in what to even teach.

    I ran office hours and evaluated assignments, and the feedback that mattered was rarely about syntax. It was about judgment: when a generated diff is worth trusting, how to review one properly, and when writing the thing yourself is simply faster than prompting for it.

  6. Software Engineer Intern

    Karsun Solutions LLC

    May 2022 – Aug 2022

    production time
    -35%
    deadline, met
    5 wk
    • React
    • Node.js
    • Python
    • SDV
    • Faker
    • AWS
    What I did

    Aimed to construct synthetic data suitable for a myriad of Machine Learning and Artificial Intelligence Use Cases, a project requiring the generation of fictitious Personal Identifiable Information based on user inputs.

    Employed React.js and Node.js for frontend development and Jest, along with Lighthouse for performance and responsiveness testing, reducing potential bottlenecks and ensuring a seamless user experience across various devices.

    Utilized libraries like Synthetic Data Vault(SDV) and the Faker library in Python, integrated with AWS services (S3, Cognito, Amplify) to optimize storage and authentication.

    Produced synthetic data with a high semblance to the authentic data enabling the client to successfully train the model within a tight 5-week deadline and reducing production time by 35%.

Featured projects

All 19 projects

Skills

AI & LLMs
  • PyTorch
  • LangChain
  • RAG
  • Embeddings
  • AI Agents
  • MCP
  • Prompt Engineering
  • Model Evaluation
ML Infrastructure
  • Data Pipelines
  • Model Deployment
  • CI/CD
  • Monitoring
  • Guardrails
  • Debugging
Programming
  • Python
  • TypeScript
  • JavaScript
  • Java
  • SQL
  • Data Structures
  • Algorithms
  • Git
Product Engineering
  • React
  • Next.js
  • Node.js
  • FastAPI
  • REST APIs
  • GraphQL
  • Full-Stack Development
Databases & Search
  • PostgreSQL
  • DynamoDB
  • MongoDB
  • Redis
  • Weaviate
  • Vector Databases
  • Retrieval
Cloud & Systems
  • AWS
  • GCP
  • GKE
  • Docker
  • Distributed Systems
  • Scalability
  • Observability

Education

Publication: Point It Out: Cross-Device Point Sharing for Public-Safety AR. IEEE WF-PST 2026, Boulder, CO.

Let's build something.

Graduating December 2026 and open to full-time software engineering roles, especially AI/ML-focused ones, from January 2027.

Contact memaytamaku.saidhwar@gmail.com