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.