Department: Engineering

Focus Areas: LLMs, Vision AI, Generative Models

Experience: 1 to 2 years

Location: Bengaluru, India

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:

• Large Language Models (LLMs) – text generation, fine-tuning, RAG systems

• Computer Vision – classification, detection, segmentation

• Generative AI – diffusion models, image generation, vision-language models

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

Key Responsibilities

Research & Experimentation

• 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

Model Development

• 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

• Fine-tune large models using LoRA, QLoRA, prompt engineering, RLHF

Engineering & Deployment

• Build end-to-end training and data pipelines

• Deploy models using FastAPI/Flask with optimized inference

• Apply quantization, pruning, distillation for model compression

• 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

• Analytical mindset for model performance debugging

• 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

ML Engineering

• 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

Interested?

Send your resume to hr@areta360.com