Senior Generative AI Engineer

Cogniify is seeking a hands-on Senior Generative AI Engineer to design, build, and deploy production-grade AI applications. The role focuses on LLM-powered products, RAG pipelines, and intelligent agent workflows, using Python alongside cloud infrastructure.

Role Focus

• Design and develop scalable LLM-powered applications in Python
• Build RAG pipelines using 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

Key Responsibilities

• 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
• Create automated evaluation frameworks to measure 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

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, 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 cloud platform: 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 technical and non-technical stakeholders

Technologies and Tools

• Python, Retrieval-Augmented Generation (RAG), embeddings, semantic search, reranking
• Frameworks: LangChain, LangGraph, LlamaIndex
• Model providers/APIs: OpenAI, Anthropic Claude, Gemini, Azure OpenAI
• Vector databases: Pinecone, Weaviate, Milvus, Qdrant, Chroma, FAISS, pgvector
• Cloud and DevOps: Docker, AWS, Azure, GCP, Kubernetes, CI/CD pipelines
• ML/Serving and ecosystems: Hugging Face Transformers, PyTorch, LoRA, QLoRA, vLLM, TGI, Ollama
• Observability/LLMOps: LangSmith, Langfuse, Arize Phoenix, MLflow, Weights & Biases
• Agent frameworks: AutoGen, CrewAI, Semantic Kernel

Benefits

• Unlimited PTO
• Very generous parental leave, above industry standards
• Entrepreneurial culture with frequent experimentation and risk-taking
• Open communication with management and company leadership
• Small, dynamic teams with high impact
• Medical, Dental and Vision coverage for employees
• Access to Disability & Life insurance
• Mental health and wellbeing support
• Annual bonus program
• Employer Stock Purchase Program (ESPP)
• Yearly team building experiences
• Mentorship and sponsorship opportunities
• Manager resources and support

What Success Looks Like

• Production-ready AI applications that are accurate, secure, and maintainable
• RAG systems that retrieve relevant information and reduce hallucinations
• AI-agent workflows that reliably complete business tasks and integrate with existing systems
• Measurable improvements in response quality, latency, and inference cost
• Clear monitoring of application performance, usage, errors, and model behaviour

Location and Compensation

• Location: Hybrid remote in the San Francisco Bay Area, CA
• Salary: USD 150,000 - 170,000 per year
• Salary range note: US East/West Coast: $150000 - $170000

Education

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