Junior AI/ML/Deep Learning Engineer

Areta360 Technologies Private Limited; Bengaluru; On-site; INR 800,000 - 1,500,000; Full time

Benefits offered by this job

Mentorship from senior AI researchers

Access to GPU resources

Collaborative R&D environment

Job summary

Areta360 Technologies Private Limited is seeking a research-driven AI Engineer specializing in LLMs and Generative Models. The role involves bridging research and engineering by implementing theoretical concepts into production-level AI systems.

Candidates should have experience in Python, PyTorch, and robust knowledge of deep learning architectures. This position offers mentorship from senior researchers and the chance to work on real-world projects.

Qualifications

• 1 to 2 years of experience required.
• Strong foundation in experimental design, baselines, and evaluation metrics.
• Ability to read and implement research papers independently.

Responsibilities

• Read, analyze, and implement state-of-the-art research papers.
• Design controlled experiments with ablation studies.
• Prototype novel architectures and training techniques.

Skills

Deep learning

Modern architectures

Applied AI

Python

PyTorch

Transformer models

Vision AI

Education

Bachelor’s/Master’s in Computer Science, AI/ML, or Data Science

Tools

FastAPI

Flask

AWS

GCP

Azure

Docker

Job description

Focus Areas: LLMs, Vision AI, Generative Models

Experience: 1 to 2 years

Role Overview

We’re looking for a research-driven AI Engineer passionate about deep learning, modern architectures, and applied AI. In this role, you’ll bridge research and engineering — implementing research papers, designing experiments, and deploying production-grade AI systems across:

This is an engineering-heavy research role requiring both theoretical depth and hands‑on implementation skills.

Key Responsibilities

• Read, analyze, and implement state-of-the-art research papers
• Design controlled experiments with ablation studies and statistical validation
• Prototype novel architectures and training techniques from recent literature
• Maintain scientific documentation of experiments, findings, and methodologies
• Build and optimize transformer-based LLMs for text generation and instruction tuning
• Develop vision models using CNNs and Vision Transformers (ViT)
• Implement generative models like Stable Diffusion and GANs
• Create multimodal AI systems (e.g., CLIP, BLIP) for vision‑language understanding
• Build end‑to‑end training and data pipelines
• Deploy models using FastAPI/Flask with optimized inference
• Ensure clean, tested, and documented code with Git version control
• Integrate models into scalable cloud environments (AWS/GCP/Azure)

Required Qualifications

Education & Core Skills

• Bachelor’s/Master’s in Computer Science, AI/ML, Data Science, or related fields
• Strong Python skills (Java is a plus)
• Proficient in PyTorch (TensorFlow familiarity a bonus)
• Solid understanding of Transformers, Attention Mechanisms, CNNs, and Vision AI

Research Capabilities

• Ability to read and implement research papers independently
• Strong foundation in experimental design, baselines, and evaluation metrics
• Excellent technical writing and documentation

LLM Expertise

• Experience with GPT-style models and encoder‑decoder architectures
• Hands‑on with fine‑tuning workflows and prompt engineering
• Understanding of RAG (Retrieval-Augmented Generation)
• Familiarity with Hugging Face Transformers & Datasets

Vision & Generative AI

• Knowledge of Diffusion Models (DDPM, Stable Diffusion)
• Understanding of ViT / ResNet / EfficientNet architectures
• Familiarity with CLIP, BLIP, and other vision‑language models
• Experience with image generation pipelines
• Strong with pandas, numpy, scikit‑learn, OpenCV
• Experience with MLflow / TensorBoard for experiment tracking
• Backend knowledge: FastAPI / Flask for serving
• Exposure to Docker and cloud platforms (AWS/GCP/Azure)
• Commitment to software engineering best practices

Preferred (Strong Plus)

• Publications or technical blogs in ML/AI
• Open-source contributions (GitHub portfolio)
• Experience with FAISS, Milvus, Pinecone
• Familiarity with LangChain, LlamaIndex, ControlNet, ComfyUI, AUTOMATIC1111
• Experience in 3D vision, video understanding, or reinforcement learning

What You’ll Gain

• Mentorship from senior AI researchers and ML engineers
• Hands‑on experience with state-of-the‑art LLMs and Generative AI
• Opportunity to work on real‑world projects across multiple industries
• Collaborative R&D environment focused on experimentation and innovation
• Access to GPU resources for large‑scale model training
• Freedom to explore and contribute new ideas to ongoing research