AI Solutions Architect ( LLM & Generative AI )

Job Title: AI Solutions Architect (LLM & Generative AI )
Location: Santa Clara, CA (Hybrid)
Duration/Term: Long Term Contract

Job Description:

We are seeking an experienced AI Architect to design and deploy scalable AI systems leveraging LLMs (GPT, LLaMA, Claude, etc.). The ideal candidate will have expertise in fine-tuning language models, optimizing Retrieval-Augmented Generation (RAG) pipelines, and developing agent-based architectures. Additionally, they will play a key role in integrating AI agents with enterprise tools and ensuring model performance optimization.

Key Responsibilities:

• LLM Development & Fine-Tuning: Build and fine-tune SLMs/LLMs using domain-specific data (e.g., ITSM, security, operations).
• RAG Optimization: Design and optimize Retrieval-Augmented Generation (RAG) pipelines with vector databases (FAISS, Chroma, Weaviate, Pinecone).
• Agent-Based Architectures: Develop agent-based AI architectures using LangGraph, AutoGen, CrewAI, or custom frameworks.
• Enterprise AI Integration: Integrate AI agents with enterprise tools (ServiceNow, Jira, SAP, Slack, etc.).
• Model Performance Optimization: Enhance AI model efficiency through quantization, distillation, batching, caching
• DevOps & MLOps Collaboration: Work closely with DevOps and MLOps teams to build CI/CD pipelines for models and agents.
• Technical Leadership: Conduct code and research reviews, mentor junior engineers, and contribute to technical strategy.

Qualifications:

Must Have:

• 5-8 years of hands-on experience in AI/ML/Deep Learning.
• Strong coding skills in Python (must); familiarity with js, Go, or Rust is a plus.
• Proficiency with PyTorch or TensorFlow.
• Deep understanding of transformer architectures, embeddings, and attention mechanisms.
• Experience with LangChain, Transformers (HuggingFace), or LlamaIndex.
• Working knowledge of LLM fine-tuning (LoRA, QLoRA, PEFT) and prompt engineering.
• Hands-on experience with vector databases (FAISS, Pinecone, Weaviate, Chroma).
• Cloud experience on Azure, AWS, or Google Cloud Platform (Azure preferred).
• Experience with Kubernetes, Docker, and scalable microservice deployments.
• Strong knowledge of REST APIs, webhooks, and enterprise system integrations (ServiceNow, SAP, etc.).
• Solid understanding of data pipelines, ETL, and structured/unstructured data ingestion.

Key Skills:

LLMs, AI Architecture, Python, PyTorch, TensorFlow, Transformer Models, LangChain, HuggingFace, LlamaIndex, RAG Optimization, Vector Databases, LoRA, QLoRA, PEFT, Azure, AWS, Google Cloud Platform, Kubernetes, Docker, Microservices, CI/CD, REST APIs, ETL, Enterprise Integrations

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