Build and maintain sophisticated, multi-step AI agents that can reason, plan, and execute tasks using tools and internal APIs
Transition AI concepts from POC to production-ready software by writing clean, maintainable, and tested Python code
Collaborate closely with Backend and Software Engineering teams to expose AI capabilities via robust and secure APIs
Implement and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and efficient retrieval strategies
Design systems with monitoring in mind, tracking the performance, cost, and accuracy of the agents you deploy
Take end-to-end ownership of agent logic, ensuring AI behavior aligns with business goals and technical constraints

3+ years of experience in Software Engineering or Data Science, with at least 1 year of strong focus on LLMs and agentic workflows
University degree in Computer Science, AI, Data Science, or comparable industry experience
Strong, production-grade Python skills with experience structuring full applications rather than just scripts or notebooks
Hands-on experience with modern LLM frameworks such as LangChain, LangGraph, LlamaIndex, or custom equivalents
Deep understanding of agentic patterns, state management, memory persistence, and orchestration of multi-step workflows
Experience with vector databases (e.g., Pinecone, Weaviate, Milvus) and retrieval systems
Experience deploying to cloud environments (preferably GCP) and integrating LLMs with external APIs and tools
Ability to reason about engineering fundamentals including system latency, token costs, rate limits, and failure recovery
A production-oriented mindset that prioritizes reliability, ownership, and solving actual business problems
Excellent knowledge of written and spoken English

Experience-based compensation package
Remote or hybrid work model
30 free days
Educational learning opportunities to support each employee's professional growth journey
Sports activities, team building, and informal gatherings
Free drinks, fruit, and ice cream

Bragg

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