Adithya S Nair / AI engineer

Engineering AIthat earnsconfidence.

I build measurable AI systems from prototype to production, combining LLM and RAG pipelines, agent memory, evaluation, full-stack engineering, and thoughtful product decisions.

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agent systems that recoverevaluation before scaletools with clear boundariesproduct thinking to production

About Adithya

Engineer, product builder, and community leader.

Full-stack AI engineer and consultant with around two years of production experience across LLM pipeline design, RAG workflows, prompt engineering, model evaluation, and FastAPI services. I also led a 200+ member ACM technical community as Chairperson, organised national-level hackathons, and mentored students in machine learning. I am now pursuing an MTech in CSE with AI and Machine Learning while continuing to ship production AI systems and grow into technical leadership.

Experience

Production work, clearly told.

Jun 2025 to May 2026

AI Engineer / Doctreen

Montpellier · Remote

Developed and maintained AI features for a medical report-generation platform, connecting model integration, evaluation, and product delivery.

Integrated OpenAI, Nebius, and OVH model APIs with prompt engineering and retrieval-augmented generation workflows.
Designed and optimised end-to-end vision analysis pipelines across 10+ imaging modalities, benchmarking model performance against radiologist-derived ground truth.
Served as acting Product Manager, conducting market research, gathering user feedback, and authoring comprehensive Product Requirements Documents.
Built internal tooling and data-processing workflows, including a token-efficient serialisation format.

Jan 2025 to Jun 2025

AI Research Intern / Doctreen

Montpellier · Remote

Contributed to the foundational development of an AI-assisted medical report system.

Implemented security guardrails and input validation for prompt injection, adversarial inputs, and healthcare data compliance.
Developed a multi-model benchmarking system to evaluate LLM performance across all pipeline stages with parallel execution.

Education

Research with an engineering base.

Jul 2026 to present

MTech · Computer Science

Artificial Intelligence & Machine LearningAmrita Vishwa Vidyapeetham

Research focus: agentic AI, memory, multi-agent systems, and LLM architecture.

Advanced study in artificial intelligence and machine learning

Sep 2021 to Aug 2025

BTech · Computer Science

Artificial Intelligence & Machine Learning · CGPA 8.48Amrita Vishwa Vidyapeetham

Built a broad computer science foundation while specialising in applied AI and machine learning.

Coursework in machine learning, deep learning, NLP, computer vision, data structures and algorithms, DBMS, and operating systems
SPARE research published with Springer at PCCDA 2026

How I work

From ambiguity to useful.

I connect product thinking, research, and engineering. I ask the difficult question early, prove the hard part quickly, and stay accountable after launch.

Frame the system

Translate an ambiguous product problem into boundaries, risks, data flows, and a buildable first version.

Architecture · Product thinking · Evaluation design

Operating thesis

A model can impress. A system must hold up.

012 yrs

shipping production AI

023

selected systems with real users

031

peer-reviewed research paper

04200+

people led in tech community

Selected systems / 03

Systems with stakes.

Developer tools, education platforms, and product delivery. Three places where reliable systems matter more than polished demos.

01 / 03Founder-led product studioSelected case

CodrantLabs

Software shipped, not strategy left in slides.

A product and AI engineering studio for teams that need senior thinking, fixed scope, and a direct path from an ambiguous problem to working software.

Visit the work

The tension

Small teams do not need more ceremony. They need someone who can connect product intent, engineering choices, AI capabilities, and the realities of delivery.

The response

Co-founded a practice spanning full-stack platforms, AI agents, RAG systems, search visibility, and technical product strategy for global clients.

What changed

A deliberately small studio model with clear ownership, fewer hand-offs, and work designed and engineered as one continuous decision process.

Product strategyAI agentsFull-stackRAGAEODelivery

02 / 03Education infrastructureSelected case

Vidyapath

One school system. Four very different realities.

A multi-school LMS for CBSE Classes 10 and 12, shaped around the daily workflows of students, teachers, parents, and administrators.

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The tension

School software often becomes a collection of disconnected forms. The real challenge is making roles, data, permissions, and AI assistance feel like one dependable system.

The response

Architected role-based authentication, multi-school provisioning, assignments, grading, attendance, timetables, audit trails, and a multi-model AI routing layer.

What changed

A single platform that reduces teacher busywork while keeping generated learning material structured, reviewable, and grounded in the school context.

Next.jsSupabaseRAGGroq + GeminiZustandFramer Motion

03 / 03Field AI prototypeSelected case

Mitti Mitra

Useful signals for decisions made in the field.

A mobile soil-health assistant that combines computer vision and multiple data sources to make practical crop guidance more accessible to Indian farmers.

Visit the work

The tension

A useful field tool has to work with incomplete inputs, communicate uncertainty clearly, and turn model output into an action someone can actually take.

The response

Designed the machine-learning pipeline and connected image analysis with supporting signals inside a lightweight Flutter and Firebase workflow.

What changed

A focused prototype that translates multi-source analysis into a simpler, field-ready decision flow rather than a technical dashboard.

PythonComputer visionFlutterFirebaseData fusionProduct design

Working toolkit / 04

Useful skills, clearly grouped.

Select an area to see the tools I use to move from an unclear problem to a dependable product.

01 / focus

AI systems

Designing reliable agentic intelligence around a real product workflow.

01Agent orchestration02RAG and reranking03MCP and tool integration04Agent memory05Evals and guardrails06Structured outputs

Volunteering and events / 04

Work beyond the desk.

Leadership, mentoring, event operations, and community programmes that strengthened how I communicate and coordinate.

2022 to present01

ACM Student Chapter, Amritapuri

Member, AI Club Mentor, Chairperson, Advisory Council

Led a 200+ member technical community for 1.5 years, organised national-level hackathons and workshops, mentored students, and now advise the next leadership team.

2022 to 202302

ICPC Asia West Regional Finals

Overall Coordinator

Coordinated participant management, event logistics, and on-ground operations for the regional finals hosted on campus.

2023 to 202403

Vidyut Multi-Fest

Core Committee and Executive Member

Managed participant accommodation and logistics for one of Kerala's largest student-run multi-fests.

202404

Decoding AI

Student Social Responsibility Project

Ran an introductory AI awareness programme for school students, making core ideas approachable through examples and discussion.

Selected experiments / 03

Built to learn.

Compact experiments that sharpened a specific technical or product skill.

202501

Namude Yatra

A multi-agent travel planner with maps and conversational replanning.

LangChainPydeckStreamlit

202402

OptiHire

Tailored job outreach, resume gap analysis, and application tracking.

NLPLLMsData

202403

Inventory Optimisation

A reinforcement learning agent for balancing stockouts, holding cost, and reorder timing.

PythonDQNReinforcement learning

Editorial illustration of a durable agent workflow moving through observable checkpoints

Field note / 002

New from the journal

Agents need a runtime.

Why durable state, resumable tasks, idempotent tools, human checkpoints, and evaluation are becoming the real architecture of agentic systems.

10 min readAugust 25, 2026

Read the field note Browse all writing

Common questions / 06

Direct answers.

The short version, for people in a hurry and the engines answering for them.

Who is Adithya S Nair?

Adithya S Nair is an AI engineer and researcher from Kottayam, Kerala, India. He builds production LLM, RAG, agentic, and full-stack systems, and has published peer-reviewed research with Springer.

What does Adithya specialise in as an AI engineer?

He specialises in LLM integration, retrieval-augmented generation, agent memory, AI evaluation, and full-stack product engineering. He ships products with Python, FastAPI, PyTorch, and Next.js, from first prototype to monitored production.

Which production AI systems has Adithya built?

Adithya co-founded CodrantLabs, a product and AI engineering studio, architected Vidyapath, a multi-school LMS with four role-specific portals, and built Mitti Mitra, a field-focused soil intelligence prototype.

Has Adithya S Nair published research?

Yes. Adithya has peer-reviewed research accepted at PCCDA 2026 and published with Springer. The work explores parameter-efficient multimodal systems designed to achieve strong results within limited compute budgets.

Is Adithya available for AI engineering roles?

Yes. Adithya is open to AI engineering roles, research collaborations, and focused product builds with real users. Email adithyasnair2021@gmail.com to start a conversation.

Where is Adithya based, and does he work remotely?

He is based in Kottayam, Kerala, India, and works remotely worldwide. His experience includes collaborating with international teams and shipping production systems across research, product, and engineering work.

The next hard thing

Let's build the hard part.

I am open to AI engineering roles, research collaborations, and focused product builds with real users and real constraints.

Start a conversationSay hello

adithyasnair2021@gmail.com LinkedIn GitHub Resume