Tamil Nadu Architectural intern Jobs – 14 Open Jobs Today

Intern - Machine Learning Engineer
Rapid Accelaration Partners —Chennai, Tamil Nadu
- System design
- Master's degree
- AWS
Quick apply
4d
Junior Architect & Architectural Intern
Beluce Architects —Nagercoil, Tamil Nadu
- ₹10,000 - ₹15,000 a month
- Full-time
- AutoCAD
- CAD
- Project management
- Paid time off
- Flexible schedule
Quick apply
Architectural Internship
Wallone Infra —Korattur, Chennai, Tamil Nadu
- ₹7,000 a month
- Full-time
- AutoCAD
- CAD
- Project management
- Paid sick time
Quick apply
Architect Intern
THE OPEN ROOM —Pallavaram, Chennai, Tamil Nadu
- ₹5,000 - ₹15,000 a month
- Full-time
- AutoCAD
- CAD
- Project management
- Paid time off
- Flexible schedule
Quick apply
Hiring Architect Intern in Coimbatore!!
APR Groups —Coimbatore, Tamil Nadu
- ₹6,000 - ₹15,000 a month
- Email
- AutoCAD
- Bachelor's degree
- In-person
Quick apply
2d
Architectural Internship
chennai architects —Adyar, Chennai, Tamil Nadu
- Up to ₹5,000 a month
- Full-time
- AutoCAD
- CAD
- Project management
Quick apply
Architect Intern
APR AGRO PVT LTD —Manamadurai, Tamil Nadu
- Full-time
- AutoCAD
- In-person
- Internship
- Food provided
Quick apply
Architect Intern
ZIONARCH ARCHITECTS —Selaiyur, Chennai, Tamil Nadu
- ₹6,000 a month
- AutoCAD
- CAD
- English
Quick apply
Architect/ Civil / Interior - Intern
Cadd Centre —Velachery, Chennai, Tamil Nadu
- ₹15,000 - ₹25,000 a month
- Part-time
- AutoCAD
- Civil engineering
- CAD
Quick apply
Junior Architect
SAB Architects —Kilpauk, Chennai, Tamil Nadu
- ₹18,000 - ₹25,000 a month
- Email
- AutoCAD
- Project management
- Bachelor's degree
- Paid sick time
Quick apply
Architect Intern
Adissia Developers —Coimbatore, Tamil Nadu
- Full-time
- AutoCAD
- Bachelor's degree
- In-person
Quick apply
Interior Architect
Vichara Design Studio —Chennai, Tamil Nadu
- ₹20,000 - ₹40,000 a month
- Full-time
- AutoCAD
- Bachelor's degree
- In-person
Quick apply
12d
Engineering Lead
Rapyuta Robotics —Chennai, Tamil Nadu
- Full-time
- C++
- Bachelor's degree
- Software development
Quick apply

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Intern - Machine Learning Engineer
Rapid Accelaration Partners —Chennai, Tamil Nadu
If that excites you, we want to hear from you. We do not expect you to rely on AI for tasks you should already understand, such as debugging your own logic,…
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Intern - Machine Learning Engineer

Rapid Accelaration Partners
Chennai, Tamil Nadu

Quick apply

Job details

Internship
4 days ago

Qualifications

Image processing
Law
CSS
MCP
Computer vision
System design
Git
Master's degree
AWS
Docker
Machine learning
JavaScript
Back-end development
REST
GitHub
APIs
JSON
Flask
AI
Communication skills
Python
HTML

Full job description

Intern - Machine Learning Engineer

Role Overview

We are looking for a hands-on Machine Learning Engineering Intern with a strong understanding of the modern AI stack. The ideal candidate understands how LLMs work, can navigate a real codebase, and is comfortable using AI tools to accelerate their work without becoming dependent on them.

You will be embedded directly in our engineering team and contribute to AI pipelines, APIs, and agent systems under the mentorship of senior ML engineers. Expect meaningful responsibility, thoughtful code reviews, and production exposure from day one.

Must-Have Requirements

The following are non-negotiable. Candidates who cannot demonstrate these will not be considered regardless of other strengths.

LLM Fundamentals & Prompting

Clear understanding of how LLMs work, including tokenization, context windows, temperature, structured output formatting, LLM guardrails, and hallucination prevention.

Proficiency in prompting techniques such as few-shot prompting, system prompts, structured outputs, role prompting, and reasoning-oriented prompts.
Familiarity with recent model releases, capability shifts, and architectural developments.
Ability to run inference on local LLMs using tools like Ollama, VLLM, or Hugging Face Transformers

AI Systems & Engineering

Moderate to strong understanding of RAG architecture, including chunking, embeddings, retrieval, reranking, and generation.

Working knowledge of AI agents, tool use, and agent orchestration frameworks such as LangChain, LlamaIndex, AutoGen, or custom frameworks.
Understanding of MCP (Model Context Protocol) and AI skills/tool design
Transformer architecture basics — attention mechanism, encoder/decoder, positional encoding, embeddings

Python & Backend Engineering

Strong Python skills, including clean, idiomatic code, proper error handling, algorithm design, and type hints.
Ability to work with structured outputs: JSON schema, Pydantic models, data validation patterns
Flask API development: REST endpoints, request/response handling, middleware
Fundamental understanding of API scaling, including async and sync patterns and basic load considerations.
Version control proficiency: Git branching, PRs, commit hygiene, resolving conflicts

AI Tools & Codebase Navigation

Ability to independently navigate an existing, non-trivial codebase using AI-assisted tools (Cursor, Claude, ChatGPT, GitHub Copilot, etc.)
Uses AI tools to boost velocity — but can reason through code independently and does not require AI to explain every line

Computer Vision & Document AI (baseline knowledge required; expertise is not mandatory)

Rough working knowledge of image processing concepts: preprocessing, transformations, color spaces
Familiarity with OCR tools and their practical limitations (Tesseract, Docling, AWS Textract, etc.).
Basic awareness of object detection concepts (bounding boxes, YOLO-style models)

Communication & Collaboration

Ability to articulate technical decisions clearly in review calls and project syncs without needing repeated prompting to explain reasoning.
Comfortable discussing system design trade-offs and architecture choices with leads and peers
Strong written communication for async updates, PRs, and documentation

Good-to-Have Skills

Candidates with these will have an advantage, but they are not disqualifying to be missing.

Docker: containerizing Python services, multi-stage builds, docker-compose for local stacks
Deep RAG expertise: Graph RAG, hybrid retrieval, vector database internals (Pinecone, Weaviate, Qdrant, pgvector)
Custom LLM agent design: memory management, multi-step reasoning, tool routing, state machines
Agent observability: tracing, logging agent runs, dashboards (LangSmith, Phoenix, custom)
LLM/VLM fine-tuning: PEFT methods (LoRA, QLoRA), GRPO, instruction tuning pipelines
Frontend / UI basics: HTML/CSS/JS or Streamlit for internal tooling and demos
Active participation in product and solution architecture discussions
Awareness of the latest releases, frameworks, and modern technologies, with the ability to choose current, practical solutions instead of relying on outdated approaches.

What We Expect From You

Independent execution

You will be assigned tasks and expected to drive them to completion with minimal hand-holding.
You should be able to take a vague requirement, ask the right clarifying questions, and convert it into working code.

AI-augmented, not AI-dependent

We expect you to use AI tools to move faster; that is the right instinct.
We do not expect you to rely on AI for tasks you should already understand, such as debugging your own logic, reading stack traces, or justifying architectural decisions.

Takes direction well, executes better

You should be able to absorb guidance from managers and tech leads and translate it into concrete action without repeated follow-up
Keep feedback loops tight by flagging blockers early and communicating progress proactively.

High standards

Our systems are client-facing, so code quality, correctness, and documentation matter.
You will be expected to review your own work before pushing it, not just submit first drafts

What You Will Work On

LLM-powered document intelligence pipelines (extraction, structuring, generation)
RAG systems with multi-source retrieval, reranking, and structured output formatting
AI agent workflows with tool use, memory, and multi-step orchestration
Flask- or FastAPI-based APIs that wrap AI capabilities for client-facing deployment
Local LLM inference setups and evaluation harnesses
Prompt engineering, evaluation, and iterative system improvement
Client-facing projects with real-world business impact
No-code and low-code solution development for internal tools and product prototypes

This internship is designed for candidates who want to build practical AI systems, learn from experienced engineers, and contribute to products that solve real client problems.

We build real AI systems. If that excites you, we want to hear from you.

Quick apply

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