• AI Engineer
  • Forward Deployed Engineer
  • SDE / Software Engineer

Open to work — Bengaluru / Hyderabad

Edara Sri Sai Kaushal

Kaushal
Edara

AI Engineer. I build production multi-agent systems that run unattended.

Portrait of Kaushal Edara
Now

AI Engineer Intern (PPO)
Kookar AI

Jun 2025 — Present

Education

IIIT Sri City
B.Tech (Hons.) CS

Nov 2022 — Jun 2026 · CGPA 9.2/10

00

Building now

01 Civic tech · Open data Ongoing

Swaram

Civic issue reporting + public accountability layer for India. Citizens photograph an issue, it gets geo‑tagged and routed to the responsible department, then tracked publicly. Includes a curated directory of 4,800+ urban local bodies, central & state grievance officers, and district‑level contacts.

Repository ↗

02 Health tech · Indian households Ongoing

Nourish

Nutrition tracker built for Indian families — snap a photo of your meal and instantly see calories, macros, micronutrients, and diet‑quality insights. Visual dashboards give you daily/weekly trends, deficiency alerts, and portion guidance so you stay in control without manual entry. Multi‑user profiles let every household member track together, and an admin console helps nutritionists or caregivers oversee plans.

Repository ↗

01

Experience

  1. Now Jun 2025
    — Present
    Internship · PPO

    AI Engineer Intern (PPO)

    Kookar AI

    Kookar runs households end to end with LLM agents — meal planning, grocery ordering, cook coordination and customer communication over WhatsApp.

    • ~20production LLM agents
    • 12systems shipped
    kookar-ai/ Twelve systems — open a node for the detail
    • agents/ Seven customer-facing systems
      • 01 grocery-order-multi-agent plan → cart → placed order

        Turns meal plans into placed grocery orders. Demand- and expiry-aware stock netting, cross-platform cart allocation, idempotent end to end.

      • 02 watcher coordination layer · 98% unattended

        Coordination layer making 20+ independently running agents act as one system per household.

        98% of dispatches run unattended

      • 03 voice-onboarding operator time 30 min → 0

        Replaced a 30-minute scheduled operator call. Resumable, stitches multi-session context, auto-triggers downstream setup.

        Built solo in four days — see case/

      • 04 onboarding-pod lead → live kitchen · 2.3× conversion

        Multi-agent across customer and cook sides. Extracts every household detail, takes a lead from first contact to a live kitchen.

      • 05 hitl-review-layer 94% corrected or blocked

        Catches agent errors before they reach the customer.

        94% of reviewed outputs corrected or blocked

      • 06 arsenal WhatsApp-only console · 2.5× per RM

        Lets RMs manage households from WhatsApp alone, without ever opening a dashboard.

      • 07 escalation 22 min → seconds · 84% auto-drafted

        Problems reach the right human in seconds instead of 22 minutes; an agent drafts the fix.

    • platform/ Five systems under the agents
      • 08 internal-console routing · overrides · review queues

        Per-agent model routing, cohort overrides, HITL review queues, plus growth and funnel analytics dashboards.

        • Routing
        • Overrides
        • Review queues
        • Growth
        • Funnel
      • 09 analytics-warehouse postgres · cohort → funnel · FinOps

        A hand-built Postgres warehouse under the dashboards: a view-migration suite plus cron scheduling covering cohort analysis, phase classification, retention, funnel and growth. On top, a reporting layer that attributes LLM cost per project from LangSmith traces — FinOps, not just charts.

        • View migrations
        • pg_cron
        • Cohort
        • Retention
        • LangSmith cost attribution
      • 10 mobile-app-payments expo / react native · razorpay

        The end-user surface. Expo / React Native with phone-OTP auth, Razorpay checkout, a transactions view, an auth-sync trigger, and an onboarding + scheduling rewrite on a custom calendar.

      • 11 llm-resilience repair · fallback stack · live registry

        Structured-output repair and recovery policies, a provider-capability model, a fallback stack, a litellm gateway behind service tokens, and model routing lifted out of code into a live agent registry with BYOK presets.

      • 12 terraform-ec2 provision → deploy → decommission

        ALB, EC2, IAM, Route53, SSL, secrets and security modules with a bootstrap and deploy script, plus OAuth login wiring. I have also decommissioned a full stack, cross-stack references included — build and teardown.

        • Provision
        • Deploy
        • Decommission
    • case/voice-onboarding-solo-4-days nothing → live TLS domain · 4 days

      My first task there: a customer-facing voice product from nothing to a live TLS domain in four days, alone, deployment included.

      Transport
      Vapi WebRTC voice sessions in the browser over a Daily.co transport — browser audio, not PSTN telephony.
      Grader
      A 33-question LLM completeness grader at temperature 0, explicitly forbidden from inferring. Fails closed, gates onboarding status at a 95% threshold.
      Archival
      Call recordings merged with ffmpeg and pushed to a transcription service.
      Delivery
      Docker + Caddy for automatic TLS, with a hand-tuned CSP — under a WebRTC transport, a policy one notch too strict yields a call that connects and then stays silent.
  2. Prior Dec 2024
    — Feb 2025
    Research internship

    Research Intern

    IIT Patna

    Fog computing for BCI healthcare, mentored by Dr. Arijit Roy. Two results — one of them peer-reviewed and published.

    R1

    DQN-LSTM scheduler for real-time EEG transmission

    Fog computing for BCI healthcare. Mean episode latency cut against the plain-DDQN baseline.

    −61%latency · Wilcoxon p = 0.001 · n = 10 seeds

    R2

    3D Pareto-optimised head selection

    Head-selection algorithm for fog-enabled EEG healthcare IoT. Balances latency, energy and reliability across the topology.

    LatencyEnergyReliability

    The paper it produced is in 03 — Publication
02

Publication

First page of the 3D-POS paper published at IEEE INDICON 2025
Publication · IEEE INDICON 2025

3D-POS: 3D Pareto Optimized Head Selection for Fog-enabled Smart EEG Healthcare IoT

E.S.S. Kaushal, R.K. Rath, S. S.R., A. Hazra. Proc. IEEE INDICON, 2025.

IEEE Xplore ↗   Written during the IIT Patna internship

03

Built

01 Deep-learning research Feb — May 2025

Morld

De-novo molecule generation — a generative-chemistry model taught to write valid molecules, then rewarded into writing good ones.

Two-stage pipeline. Stage 1 trains an LSTM on 1M+ SMILES for next-token prediction. Stage 2 fine-tunes it with REINFORCE against a custom reward combining validity, novelty, fragment presence and QED. Top-p (nucleus) sampling for generation diversity, plus fragment-based control to steer toward a scaffold. An automated evaluation harness scores validity, novelty, uniqueness, diversity and QED.

96%
Chemical validity
100%
Novelty
100%
Uniqueness
0.85
QED

Stack TensorFlow 2.19 · RDKit · Python 3.10 · LSTM · REINFORCE Repository ↗

02 Deployed full-stack product Nov 2024 — Apr 2025

Hunger Hearts

Food redistribution platform · live on Render + Vercel

  • 78Mtonnes of food wasted a year
  • 500,000child deaths — malnutrition a factor
  • 194Mundernourished

Moves surplus food from restaurants and households to families in slums, matching every donation to the nearest delivery partner with race-safe handoffs. Express + MongoDB behind a Next.js frontend, JWT auth with bcrypt. Redis on Upstash caches the post feed with write-through invalidation on new posts; MongoDB indexes cut query time. Swagger docs for every route at /api-docs, Jest unit tests, a Dockerised backend shipped to Docker Hub and deployed on Render via GitHub Actions, frontend on Vercel.

Source Race-safe donation handoff · write-through cache invalidation Repository ↗

Pipeline
  1. Next.js
  2. Express
  3. MongoDB
  4. Redis · Upstash
  5. Jest
  6. Swagger
  7. Docker Hub
  8. GitHub Actions
  9. Render + Vercel
03 Systems benchmark · open source Dec 2024 — Mar 2025

lstm-art-drl / fog-task-scheduler

LSTM + Fuzzy-ART deep RL scheduler with per-prototype replay buffers, driving a discrete-event simulator under Poisson node churn. Separate fog-head and fog-node models, a baselines suite and a benchmark harness in-repo.

  • 6baselines compared
  • 10seeds · paired trials
Method
LSTM + Fuzzy-ART deep RL · per-prototype replay buffers
Environment
Discrete-event simulator · Poisson node churn
Models
Separate fog-head and fog-node policies
Evaluation
Benchmarked against 6 baselines over 10-seed paired trials

Stack Python · Deep RL · LSTM · Fuzzy-ART · discrete-event simulation Repository ↗

04

Stack

Languages
  • Python
  • JavaScript / TypeScript
  • PostgreSQL
Frameworks
  • FastAPI (async)
  • Express.js
  • LangGraph
  • LangChain
  • Next.js
  • React
  • CSS
  • DSPy
Tools
  • Git
  • GitHub
  • Docker
  • Kubernetes
  • Terraform
  • Vapi SDK
  • LangSmith
  • LiteLLM
  • Postman
  • CI/CD
Platforms
  • AWS (ECS, Lambda, SQS, Bedrock)
  • Supabase
  • Redis
  • OpenAI
  • Anthropic
  • Gemini
  • Kimi
  • OpenRouter
  • ElevenLabs
  • Deepgram
  • Razorpay
  • Exotel
  • WhatsApp (wuzapi)
Hard skills
  • LLM Integration
  • RAG
  • Vector Databases
  • Multi-Agent Orchestration
  • Voice AI (STT/TTS)
  • Backend Engineering
  • Human-in-the-Loop Systems
  • Systems Architecture
  • Dynamic System Design
Soft skills
  • Figure-it-out mindset
  • Ownership
  • On-time delivery
  • Fast and accurate execution
  • Parallel execution across workstreams
05

Record

Nov 2022
— Jun 2026

IIIT Sri City, Andhra Pradesh

B.Tech (Hons.) Computer Science.

9.2/ 10 CGPA

Aug 2024
— May 2025

Technical Secretary, IIIT Sri City

Directed 10 technical clubs and ran the central and alumni portals.

1,500+ students4,200+ alumni6,500+ at Abhisarga fest

10clubs

Jul 2023
— Aug 2024

Core Member, IOTA Club

Ran workshops, hackathons and contests for the campus developer community.

2+ workshops3 hackathons12 coding contests

200+students

06

Engineering signatures

01

Hunts silent failures

Swallowed exceptions · a cached False with no TTL · RLS on with no policies. Every fix ships instrumentation.

02

Moves enforcement where it can't be bypassed

Triggers · CHECK constraints · partial unique indexes. Make the bad state unrepresentable.

03

Deletes on runtime evidence

Traces decide, not static analysis. A path comes out once production shows nothing reaches it.

04

Validates against production reality

Anchored to a real broken row, proved inside a transaction I then rolled back.

05

Writes the deploy order down

Code ↔ migration ordering, stated — including the windows where the database would outrun the app.

06

Configures through data, not code

HITL settings · playbooks · model routing — tables operators edit without a deploy.

Also in the record
  • Postgres triggers as an enforcement boundary
  • RPC state machines with pg_net callbacks
  • pg_cron scheduling
  • Stalled-stage reapers
  • SCD Type 2 versioning
  • Append-only audit tables
  • RLS policies
  • Correlation-ID lifecycles
  • Terminal-event chaining over timers
  • Redis debounce coordination
  • Join barriers
  • Single-flight
  • Per-household locks
  • Circuit breakers
  • Idempotent EventBridge / SQS scheduling
  • Exotel two-leg bridging
  • WhatsApp Business webhooks
  • Slack Events API