Index№ 001 · Portfolio
PracticeForward Deployed AI
Edition2026 · Current
AvailabilityOpen to Engagements

Prayas Jain.

A Forward Deployed AI Engineer who embeds with your team, scopes the real problem, and ships production LLM systems — from first discovery call to live deployment.

See selected work Start a project
Indore · IN — Working w/ teams in US · EU · APAC Scroll v.2026.07 — Last shipped:
Forward Deployed Delivery Discovery → Production Multi-Agent Orchestration Retrieval-Augmented Generation LangGraph Pipelines Full-Stack AI SaaS Forward Deployed Delivery Discovery → Production Multi-Agent Orchestration Retrieval-Augmented Generation LangGraph Pipelines Full-Stack AI SaaS
01 / Position
Opening Statement

Deployed
where the problem
lives.

Practice Forward Deployed AI · LLM Systems Based Indore, IN — Global Engaged via Freelance · Contract · Embedded Full-time Stack Python · TypeScript · Cloud

Two years deep, the lesson is clear: most AI projects die between the demo and the deploy. I'm the engineer companies deploy into that gap.

I sit in your discovery calls, map the real workflow with your stakeholders, then architect multi-agent pipelines and RAG systems wired into APIs that hold under live traffic — and iterate them against real usage, not assumptions.

Field record: scoped evaluation rubrics with a recruiting team and shipped an interview platform running 50+ live sessions a day; mapped a lender's follow-up ops on-site and rebuilt them event-driven; turned merchant discovery sessions into a multi-agent marketing engine. Each one in production. Each one measured.

2+
Years shipping
production AI
12+
Systems live
across 4 verticals
50/d
AI interview sessions
handled per day
99%+
Uptime SLA
maintained in prod
02 / Services
What I Deploy

Four ways
to work
together.

S—01 · Embed

Discovery &
solution architecture

I embed with your stakeholders, map the real workflow, and design the AI architecture that fits it. For teams about to commit serious budget — and not wanting to commit it wrong.

  • Stakeholder discovery
  • Requirements scoping
  • Architecture design
  • Build vs. buy
S—02 · Build

RAG & knowledge
systems

Context-aware retrieval systems that turn your messy corpus — docs, tickets, transcripts, code — into a precise, hallucination-resistant answer engine.

  • Vector DB design
  • Chunking + reranking
  • Hybrid retrieval
  • Evals + observability
S—03 · Build

Multi-agent orchestration

LangGraph-based agent systems that plan, route, call tools, and complete real multi-step workflows. With guardrails, retries, and a debug trail you can read.

  • LangGraph / LangChain
  • Tool use + planning
  • Function calling
  • State + checkpointing
S—04 · Deliver

End-to-end AI SaaS
& deployment

The whole stack: FastAPI backend, React/Next.js frontend, Postgres + Redis, Stripe, auth, deploy — then post-launch iteration against real usage. Zero to live in weeks.

  • FastAPI · Next.js
  • Stripe + Auth
  • Cloud deploy
  • Post-launch iteration
03 / Case Files
Selected Work · 2024–2026

Production
artifacts,
not demos.

01CF—01
Marketing AI

Multi-agent RAG engine for an eCommerce marketing platform

Forward Deployed AI Engineer · AiTrillion · 2026 — Present

Forward-deployed owner of GenAI for a large eCommerce marketing automation platform: scoped AI use cases directly with global merchants and internal stakeholders, then architected production RAG pipelines with hybrid retrieval and a LangGraph multi-agent orchestrator automating multi-step marketing workflows. Closed the loop between merchant feedback and shipped improvements; set the org's standard for LLM integration, evals and AI safety.

LangGraphRAGFastAPIVector DBOpenAIAnthropic
0→1
AI platform
delivery
Multi-step
Marketing workflows
fully automated
Production
Live, integrated
across product
02CF—02
AI Interview Platform

LLM-powered interview platform running 50+ daily sessions

Client-Facing AI Delivery · Techdome Solutions · 2024 — 2026

Led end-to-end build of an AI interview platform on LLM APIs + FastAPI — scoping evaluation rubrics on live calls with the client's recruiting team and iterating prompts on real transcripts. Structured candidate evaluation, automated reports, async pipelines: recruiter workload down 40% at 50+ live sessions a day in production.

FastAPILLM APIsAsync PythonPostgresDockerAzure
−40%
Recruiter
workload reduced
50/day
Live AI interview
sessions handled
−35%
API response
latency cut
03CF—03
Voice + Automation

Event-driven WhatsApp automation for real-estate lending

Backend / AI Engineer · Techdome Solutions · 2025

Mapped the client operations team's loan follow-up workflow in discovery sessions, then designed and deployed an event-driven backend on Apache Kafka + webhooks automating WhatsApp follow-ups for a real-estate lending platform — message templates iterated against production feedback until manual outreach was cut in half.

Apache KafkaWebhooksWhatsApp APIEvent-DrivenFastAPIRedis
−50%
Manual follow-up
operations cut
Event-driven
Kafka + webhook
backbone
99%+
Production
uptime SLA
04CF—04
Freelance · SaaS

AI-powered trade analysis & journal SaaS

Forward Deployed AI Engineer & Consultant · Independent · 2024 — Present

Subscription SaaS with real-time analytics, performance tracking, portfolio insights and Stripe billing — built end-to-end on a React + FastAPI + Postgres stack. Plus: an OpenAI Assistants automation engine for long-running, multi-step ops workflows, and a production-grade marketplace with admin + user portals and PCI-compliant payments.

ReactFastAPIPostgresStripeOpenAI AssistantsRBAC
Live
Subscription SaaS
shipped solo
Multi-product
SaaS · Marketplace
Automation
PCI
Compliant payment
integration
04 / Process
The forward deployed loop

Embedded from
discovery to
deployment.

Step 01 →

Embed &
discover

I sit with your stakeholders — not just your engineers. Map the real workflow, the constraints, and what "shipped" actually means for you. You leave with a one-pager and a fixed quote.

Week 0
Step 02 →

Scope &
architect

Data flow, model choices, infra, evals, risk. A short tech spec you and your team can challenge before a line of code gets written.

Week 1
Step 03 →

Build in
the open

Weekly demos to the people who'll use it. Code on your repo. Working software end-to-end early — quality, latency, cost refined from a real baseline, not a guess.

Weeks 2 — N
Step 04 →

Deploy &
iterate

Ship, instrument, document — then tune against real usage, logs, and user feedback on the ground. Optional retainer for evolution and on-call. You own the system either way.

Launch + 30 days
05 / Stack
Daily tools of the trade

A pragmatic
toolkit for
real systems.

AI & LLM
  • OpenAI API
  • Anthropic API
  • Google Vertex AI
  • LangChain · LangGraph
  • Hugging Face
  • Fine-tuning & RLHF
  • Prompt engineering
  • Embeddings · Semantic search
Backend
  • Python · TypeScript
  • FastAPI · Django
  • REST · GraphQL
  • Microservices
  • Apache Kafka
  • WebSockets
  • Celery
  • JWT · OAuth 2.0
Frontend
  • React.js
  • Next.js
  • TypeScript
  • Tailwind CSS
  • Responsive UI
  • Component design
  • Real-time UIs
  • Dashboard analytics
Data & Infra
  • PostgreSQL · MongoDB
  • Redis
  • FAISS · Pinecone
  • ChromaDB · Weaviate
  • AWS · Azure · GCP
  • Docker · Kubernetes
  • GitHub Actions · CI/CD
  • Nginx · Linux
The work consistently holds up in production — not just demos. That's the part most AI teams skip. Prayas doesn't.
— Internal recognition · Applause & Spot Awards · Techdome Solutions
06 / Record
Education · Awards · Certifications

Quiet
credentials.
Loud work.

Education
B.Tech, Artificial Intelligence & Machine Learning
Indore Institute of Science & Technology · Indore, India
Aug 2021 — Aug 2025
Awards
Applause Award
For outstanding cross-team collaboration and measurable impact across engineering, product and operations.
Techdome · 2025
Spot Award
For exceptional delivery under pressure — shipping critical features on tight timelines without compromise.
Techdome · 2025
Certifications
Data Science Job Simulation
Applied data science workflow simulation with industry-graded deliverables.
BCG · via Forage
Breaking into Project Management
Foundations of structured PM practice for cross-functional product delivery.
GeeksforGeeks
07 / Engagement
Ways to work together

Pick the
shape of the
collaboration.

A few formats — picked by what you actually need. The shortest is a sharp two-week sprint; the longest is an embedded multi-month deployment. Also open to contract placements (direct or via staffing partners) and full-time Forward Deployed Engineer roles. Everything below assumes async-first, weekly demos, and your team owning the system at the end.

Tier · 01

Sprint

For teams that need a sharp, fixed-scope intervention.
  • 2-week focused engagement
  • Architecture audit + spec
  • One shipped capability or prototype
  • Async + 2 live calls / week
Duration · 2 weeks
Most asked
Tier · 02

Project

For teams shipping a complete AI capability end-to-end.
  • Full design → build → deploy
  • RAG / Agents / Voice / SaaS
  • Production-ready code on your repo
  • 30-day post-launch support
Duration · 4 — 12 weeks
Tier · 03

Embedded

For teams that need a forward deployed engineer over the long arc.
  • Embedded in your team & with your customers
  • Roadmap, builds, on-call
  • Stakeholder demos + discovery
  • Retainer · contract · full-time
Duration · 3+ months
Do you work solo or as part of a team?+
Both. I can be the only AI engineer on a small product team, or forward-deploy into a larger engineering org and own the AI surface area end-to-end. I bring trusted collaborators (frontend, design, infra) when the scope needs them.
Do you take contract or full-time embedded roles?+
Yes. Beyond freelance sprints and projects, I take contract placements (direct or via staffing partners) and full-time Forward Deployed Engineer roles — embedded with your customers or your product team, remote or hybrid.
What does a typical week look like?+
Async-first work on your repo. One or two live calls a week for direction and demos. Every Friday: a working build, written update, and the next week's plan. You're never guessing where it stands.
Which LLM providers do you build on?+
Provider-agnostic by default — OpenAI, Anthropic, open-weights via Hugging Face. Choice driven by your latency, cost, privacy and quality constraints, not by hype. I'll usually run a bake-off in week one.
What about code ownership & IP?+
You own everything — code, weights, prompts, infra. I work on your repo, your cloud, your accounts. Standard mutual NDA available. Nothing of yours ends up in a personal sandbox.
What's the fastest we can start?+
Discovery call within 48 hours. Spec + quote within a week of that. Kick-off as soon as the scope is signed — usually within 1–2 weeks of first contact, subject to current capacity.

Got a hard
AI problem?
Let's talk slides.
Let's ship it.

prayas1711@gmail.com Download résumé
Email
prayas1711@gmail.com
Phone · WhatsApp
+91 79700 33564
LinkedIn
/in/prayasjain17
GitHub
@prayas17
X · Twitter
@prayas17jain
Instagram
@prayas17_
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