AI Lead Manager - Ripplehire

Non-Negotiable Skills

Python (real coding ability, not theoretical)

Hands-on LLM integration (not just coursework)

Cloud deployment experience (AWS or Azure)

RAG understanding

API development experience

Role Summary

We are looking for a motivated Junior AI Engineer with hands-on experience in Python and cloud platforms (AWS or Azure) to help design, build, and deploy Generative AI solutions. This role will work closely with senior engineers, data teams, and business stakeholders to build AI-powered applications including LLM integrations, RAG pipelines, prompt workflows, and AI-driven automation solutions. This is a hands-on engineering role — not research-only.

Key Responsibilities

Generative AI development in this role involves building and integrating applications that leverage large language models. The engineer will be responsible for developing retrieval-augmented generation (RAG) pipelines, implementing prompt engineering techniques, and ensuring structured output generation. A key part of the work will be integrating AI services through APIs such as OpenAI, Azure OpenAI, or AWS Bedrock, while also creating AI-powered chatbots, assistants, and internal productivity tools that enhance business workflows.

Python engineering is central to the position. The candidate will be expected to write clean, scalable code that supports AI workflows, while also developing REST APIs using frameworks like FastAPI or Flask. Working with JSON, structured data, embeddings, and vector stores will be routine, as will building data processing scripts that feed into AI pipelines. This requires both technical precision and adaptability to evolving AI technologies.

Cloud deployment is another critical responsibility. The engineer will deploy AI applications using AWS services such as S3, Lambda, Bedrock, EC2, and API Gateway, or Azure services including Azure OpenAI, Functions, Blob Storage, and App Services. Containerization with Docker, even at a basic level, will be necessary to support scalable deployments. In addition, the role requires supporting CI/CD pipelines to ensure smooth and reliable cloud-based operations.

Finally, data and integration tasks will play a significant role. The engineer will work with both structured and unstructured data sources, build connectors to databases such as

PostgreSQL, MySQL, or SQL Server, and assist in creating vector databases using FAISS, Pinecone, or OpenSearch. Supporting model evaluation and logging will also be part of the responsibilities, ensuring that AI systems are monitored, tested, and continuously improved.

Required Skills