Company Description

Dimaag is a leading design and technology company that specializes in AI solutions across multiple industry verticals including Smart Factory. Established in 2018 and headquartered in Silicon Valley with offices in Osaka, Japan, and Bangalore, India, Dimaag's EV business unit has a strong presence in deployed cutting edge industry solutions through its proprietary ENCORE ecosystem of EV components and charging solutions. Join Dimaag in its mission to create sustainable, high-performance technology for a better future.

Role Description

This is a full-time, on-site role for an AI Engineer (Generative AI & RAG Specialist) based in Fremont, CA. The AI Engineer will focus on building and optimizing state-of-the-art generative AI and retrieval-augmented generation (RAG) models. This individual will design and deploy scalable production systems using Large Language Models (LLMs). with a focus on building robust Retrieval-Augmented Generation (RAG) pipelines and optimizing transformer-based architectures to solve complex problems.

Key Responsibilities

Architect RAG Pipelines: Develop and optimize end-to-end RAG systems for multimodal data, including document parsing, embedding strategies, and vector database management.

LLM Implementation: Select, fine-tune, and deploy LLMs (OpenAI, Anthropic, Llama, etc.) using frameworks like LangChain or LlamaIndex.

Model Optimization: Work with transformer architectures to improve inference speed and accuracy (quantization, pruning, or prompt engineering).

Data Engineering: Manage unstructured data workflows and high-dimensional vector search (e.g., Pinecone Qdrant, Weaviate, or Milvus).

Required Skill Set

Core AI: Deep understanding of the Transformer architecture (Attention mechanisms, encoders/decoders).

Frameworks: Proficiency in PyTorch or TensorFlow, and orchestration tools like LangChain.

Vector DBs: Hands-on experience with vector similarity search and indexing.

Programming: Expert-level Python and experience with API integration.

Deployment: Familiarity with cloud AI services (AWS Bedrock, GCP Vertex AI, or Azure AI).

Preferred Qualifications

Experience with fine-tuning techniques like LoRA or QLoRA.

Contributions to open-source AI projects or research publications.

Knowledge of evaluation frameworks for LLMs (e.g., RAGAS or TruLens).