Вакансия Full-stack developer / AI engineer

Skills

• OpenAI API
• Microservices
• CI/CD
• LLM
• Slack API
• RAG
• LangChain
• LlamaIndex

Описание вакансии

We are seeking an engineer who is ready to become the technical backbone of our new internal AI product. This is a startup project where not only deep technical expertise is essential, but also flexibility in thinking, decision-making speed, and adaptability to changing priorities based on product development needs.
Initially, you will analyse the existing MVP, expand its architecture, implement critical business functions, and integrate external systems. Upon achieving success, you will transition into the role of Technical Lead with the opportunity to assemble your own team.
We need a responsible, agile, and holistic thinker who wants to contribute to developing something significant and genuinely useful for the world.

Tech Stack

• Programming Language: Python
• Frameworks: FastAPI (priority), Flask
• AI/LLM: OpenAI API (chat, functions, assistants, context, memory)
• Integrations & Automation: REST API, Slack API, GraphQL, Selenium, Playwright
• Vector Databases (preferred): FAISS, Pinecone, Weaviate
• AI Libraries (preferred): LangChain, LlamaIndex
• Databases: PostgreSQL, MongoDB
• DevOps/Infrastructure: Docker, GitHub Actions, CI/CD pipelines, deployment on VPS / GCP / AWS

Requirements

• Profound knowledge of OpenAI API, including handling functions, instructions, agents, memory, and contexts
• Understanding and experience with microservices architecture
• Experience integrating external services via APIs
• Basic DevOps skills: environment setup, CI/CD, deployment, log management
• Ability to work autonomously, think critically, and rapidly test ideas
• Initiative and adaptability in a startup environment

Will be a plus

• Experience building AI products or agents based on LLM
• Experience working with Slack API or other chat platforms (bots, integrations, automation)
• Knowledge and practical experience with vector databases and Retrieval-Augmented Generation (RAG)
• Practical use of LangChain, LlamaIndex, or similar frameworks
• Understanding of knowledge graphs, contextual queries, and embedding pipelines
• Experience forming technical teams or mentorship