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Machine Learning Hardware Engineer (Remote - India) at Jobgether. This position is posted by Jobgether on behalf of a partner company. We are currently looking for a . Machine Learning Hardware Engineer. in . India. .. In this role, you will be responsible for designing, developing, and optimizing enterprise-grade retrieval-augmented generation (RAG) systems and AI infrastructure. You will work on end-to-end AI pipelines, integrating large language models with advanced retrieval systems, and ensuring high-performance, scalable solutions in production. The position involves hands-on development in Python and Golang/Rust, deploying solutions on cloud platforms, and implementing hybrid search, embeddings, and semantic ranking strategies. You will collaborate with cross-functional teams, guide other engineers, and implement best practices for AI workflows and RAG architectures. This is a high-impact role for candidates passionate about pushing the boundaries of AI hardware and software integration while working in a fast-paced, innovative environment.. . Accountabilities:. Lead the design and implementation of RAG architectures to ensure reliability, scalability, and low-latency performance.. Develop and optimize multi-stage AI pipelines using LLM orchestration frameworks such as LangChain, LangGraph, or LlamaIndex.. Build high-performance services and APIs in Python and Golang/Rust to support AI workflows, document ingestion, and retrieval processes.. Implement hybrid search strategies, vector embeddings, and semantic ranking to improve contextual accuracy.. Design, iterate, and optimize prompts for domain-specific applications, including few-shot and tool-augmented prompts.. Collaborate with cross-functional teams, mentor engineers, and establish KPIs for RAG pipeline and model performance.. 8+ years in software engineering or applied AI/ML, with at least 2+ years focused on LLMs and retrieval systems.. Strong proficiency in Python and Golang or Rust, with experience building high-performance services and APIs.. Expertise in RAG frameworks (LangChain, LangGraph, LlamaIndex) and embedding models.. Hands-on experience with vector databases (Databricks Vector Store, Pinecone, Weaviate, Milvus, Chroma).. Solid understanding of hybrid search (semantic + keyword) and embedding optimization.. Experience with cloud ML deployment, preferably AWS and Databricks.. Knowledge of LLM fine-tuning (LoRA, PEFT) and knowledge graph integration is a plus.. Strong problem-solving, decision-making, and communication skills for cross-team collaboration.. Bachelor’s degree required; Master’s or PhD in CS or related fields preferred.. . Company Location: India.