Machine Learning Hardware Engineer

Most Important Skills/Responsibilities: Lead RAG Architecture Design – Define and implement best practices for retrieval-augmented generation systems, ensuring reliability, scalability, and low-latency performance. Full-Stack AI Development – Build and optimize multi-stage pipelines using LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, or custom). Programming & Integration – Develop services and APIs in Python and Golang to support AI workflows, document ingestion, and retrieval processes. Search & Retrieval Optimization – Implement hybrid search, vector embeddings, and semantic ranking strategies to improve contextual accuracy. Prompt Engineering – Design and iterate on few-shot, chain-of-thought, and tool-augmented prompts for domain-specific applications. Strong proficiency in Python and Golang or RUST, with experience building high-performance services and APIs. Need experience in developing end-to-end projects -> backend software(RAG/GenAI, text/image, model improvement, scoring) -> AWS/databricks(deployment) -> endpoint -> chatbot Key Responsibilities Lead RAG Architecture Design – Define and implement best practices for retrieval-augmented generation systems, ensuring reliability, scalability, and low-latency performance. Full-Stack AI Development – Build and optimize multi-stage pipelines using LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, or custom). Programming & Integration – Develop services and APIs in Python and Golang to support AI workflows, document ingestion, and retrieval processes. Search & Retrieval Optimization – Implement hybrid search, vector embeddings, and semantic ranking strategies to improve contextual accuracy. Prompt Engineering – Design and iterate on few-shot, chain-of-thought, and tool-augmented prompts for domain-specific applications. Mentorship & Collaboration – Partner with cross-functional teams and guide engineers on RAG and LLM best practices. Performance Monitoring – Establish KPIs and evaluation metrics for RAG pipeline quality and model performance. Qualifications Must Have: 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). Strong understanding of hybrid search (semantic + keyword) and embedding optimization. Bachelors degree required Preferred: LLM fine-tuning experience (LoRA, PEFT). Knowledge graph integration with LLMs. Familiarity with cloud ML deployment (AWS (preferred), Databricks, Azure). Masters or PHD degree in CS Soft Skills Strong problem-solving and decision-making skills under tight timelines. Excellent communication for cross-functional collaboration. Ability to work independently while aligning with strategic goals.

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