Machine Learning Hardware Engineer
3 days ago
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 -> chatbotKey ResponsibilitiesLead 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.QualificationsMust 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 requiredPreferred: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 CSSoft SkillsStrong 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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