Senior Software Engineer - AI/ML

Gruve•Stanford, CA

11h

About The Position

We are seeking a highly skilled Senior Software Engineer - AI/ML to architect and deliver enterprise-grade AI solutions within a complex healthcare environment. This role focuses on designing, building, and deploying Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) systems that integrate securely and seamlessly into clinical and operational workflows. The ideal candidate brings deep expertise in transformer-based models, production-scale ML systems, and cloud-native architectures, with experience operating in regulated environments such as healthcare. This is a hands-on technical leadership role requiring ownership of the full AI lifecycle—from design through deployment and optimization.

Requirements

5–8+ years of experience in AI/ML engineering or related roles.
Strong foundation in machine learning, deep learning, and transformer architectures.
Hands-on experience with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems.
Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
Experience working with vector databases (e.g., FAISS, Pinecone, Milvus, Weaviate).
Experience designing and deploying production-grade AI systems.
Familiarity with cloud platforms (AWS, Azure) and containerized deployment models.
Experience operating in regulated environments with healthcare compliance standards (HIPAA or similar).
Strong problem-solving skills and cross-functional communication abilities.

Nice To Haves

Experience designing hybrid Vector + Graph RAG architectures.
Hands-on experience with knowledge graph design and graph databases (Neo4j, RDF/SPARQL, Cypher).
Expertise in advanced fine-tuning techniques such as LoRA and Q-LoRA.
Experience implementing LLM evaluation frameworks and hallucination detection systems.
Background in healthcare AI systems or clinical data integration.
Experience building scalable microservices architectures for AI platforms.
Prior experience mentoring engineers or leading AI architecture initiatives.

Responsibilities

Architect and deliver scalable AI/ML solutions with emphasis on LLMs, RAG architectures, and deep learning systems.
Own the full AI lifecycle including data ingestion, document indexing, embedding generation, retrieval design, preprocessing, fine-tuning, evaluation, and production deployment.
Design and optimize RAG pipelines leveraging vector databases (FAISS, Pinecone, Milvus, Weaviate) and frameworks such as LangChain and LlamaIndex.
Implement advanced fine-tuning methodologies including LoRA and Q-LoRA for domain-specific transformer optimization.
Develop hybrid RAG + reasoning workflows for complex enterprise use cases.
Curate and manage structured and unstructured healthcare datasets; implement chunking, embedding, and retrieval strategies to enhance contextual accuracy.
Establish robust evaluation frameworks measuring retrieval accuracy, faithfulness, latency, hallucination rates, and response relevance.
Optimize model performance through embedding tuning, reranking strategies, inference optimization, and efficient compute utilization.
Build and maintain MLOps / LLMOps pipelines covering CI/CD, deployment automation, monitoring, drift detection, and continuous improvement.
Deploy AI services across AWS and Azure in secure cloud-native and hybrid architectures.
Develop APIs and microservices to integrate AI capabilities into enterprise healthcare systems.
Ensure HIPAA-aligned data security, privacy, and regulatory compliance standards.
Collaborate with cross-functional stakeholders including clinical, product, engineering, and compliance teams.
Mentor engineers and establish best practices in AI architecture and production-grade ML systems.