Senior Generative AI Engineer — RAG & AI Agents (Hybrid SF)

Cogniify; San Francisco (CA); Hybrid; USD 150,000 - 170,000; Full time

Benefits offered by this job

Unlimited PTO

Very generous parental leave

Annual bonus program

ESPP

Team building experiences

Job summary

Cogniify in the San Francisco Bay Area is hiring a hands-on engineer to design, build, and deploy LLM-powered systems for production-grade AI applications. You will work across the full lifecycle from model choice to deployment and monitoring in a hybrid work setting.

The role emphasizes building RAG pipelines, intelligent agent workflows, and robust integrations with internal tools and APIs, with a focus on accuracy, latency, cost, and security while collaborating with product and engineering

Qualifications

• Bachelor's or Master's degree in CS, Engineering, or a related discipline, or equivalent practical software-development experience.
• 6-9 years of professional software-development experience, including strong hands-on experience with Python.
• Experience developing backend services and integrating REST APIs.
• Hands-on experience building LLM or Generative AI applications.
• Practical experience implementing RAG using embeddings, semantic search, and vector databases.
• Experience with LangChain, LangGraph, LlamaIndex, or a comparable LLM application framework.
• Experience integrating foundation models through APIs such as OpenAI, Anthropic Claude, Gemini, or Azure OpenAI.
• Working knowledge of vector databases including Pinecone, Weaviate, Milvus, Qdrant, Chroma, FAISS, or pgvector.
• Experience with Docker and deployment on at least one of AWS, Azure, or GCP.
• Understanding of prompt engineering, hallucination reduction, output validation, and LLM evaluation.
• Strong understanding of software engineering practices including Git, testing, debugging, and clean code.
• Ability to communicate technical solutions clearly to both technical and non-technical stakeholders.

Responsibilities

• Design and develop scalable LLM-powered applications using Python.
• Build RAG pipelines including document processing, embeddings, vector databases, semantic search, and reranking.
• Develop AI-agent and multi-agent workflows with tool calling, memory, orchestration, and human approval steps.
• Integrate LLMs with internal systems, external APIs, databases, and enterprise applications.
• Evaluate and select foundation models based on accuracy, latency, cost, security, and business needs.
• Improve prompt quality, retrieval accuracy, response time, and token usage.
• Implement safety guardrails, output validation, access controls, and fallback mechanisms.
• Build automated evaluation frameworks to assess response quality, hallucination, relevance, and reliability.
• Containerize applications with Docker and deploy on AWS, Azure, or GCP.
• Apply monitoring and LLMOps practices for model performance, cost, latency, errors, and production usage.
• Collaborate with product, engineering, and business teams to translate requirements into reliable AI solutions.
• Document technical architecture, design decisions, APIs, and operational processes.

Education

Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline

Job description

Cogniify in the San Francisco Bay Area is hiring a hands-on engineer to design, build, and deploy LLM-powered systems for production-grade AI applications. You will work across the full lifecycle from model choice to deployment and monitoring in a hybrid work setting.

The role emphasizes building RAG pipelines, intelligent agent workflows, and robust integrations with internal tools and APIs, with a focus on accuracy, latency, cost, and security while collaborating with product and engineering