Senior AI/LLM Engineers with strong Python experience, skilled in designing, building, and productionizing LLM-powered applications and AI systems at scale. It's a great fit for people who enjoy working at the intersection of software engineering and applied AI, and who take ownership from prototyping through production deployment.

• What you’ll build and ownDesign, build, and deploy LLM-powered features and applications using Python.
• Develop and maintain backend services and APIs (e.g., FastAPI) that expose AI/LLM capabilities to other systems.
• Build and optimize RAG pipelines, including embeddings, vector search, and retrieval strategies.
• Design, test, and iterate on prompts, agents, and orchestration flows using frameworks such as LangChain, LlamaIndex, or similar.
• Integrate with LLM providers and APIs (e.g., OpenAI, Anthropic, open-source models) and manage tradeoffs around cost, latency, and quality.
• Evaluate model outputs systematically, building tooling and metrics to test accuracy, safety, and regression across iterations.
• Work with containerized environments and data infrastructure (e.g., PostgreSQL, Redis, vector databases) to support reliable AI systems in production.
• Collaborate with cross-functional stakeholders to translate ambiguous product needs into technically sound AI solutions.

• Core Requirements Strong proficiency in Python and experience with FastAPI or similar backend frameworks.
• Experience working with LLM APIs (e.g., OpenAI, Anthropic, or similar) and frameworks such as LangChain or LlamaIndex.
• Experience with RAG architectures, embeddings, and vector databases (e.g., Pinecone, Weaviate, pgvector, or similar).
• Hands-on experience with Docker and containerized development environments.
• Experience working with PostgreSQL, Redis, or similar data stores.
• Strong experience writing functional and integration tests, including evaluation frameworks for AI/LLM output quality.
• Excellent written and verbal communication skills in English.
• Ability to work independently in a remote, fast-paced environment.

• Nice-to-HaveExperience fine-tuning or evaluating open-source LLMs.
• Familiarity with prompt engineering best practices and agentic workflows.
• Experience with distributed systems, streaming (e.g., Kafka), or large-scale applications.
• Background in machine learning fundamentals (e.g., scikit-learn, PyTorch, or TensorFlow).
• Comfortable working flexible hours to overlap with distributed teams across different time zones.

Additional