What You'll Do
Lead the creation and implementation of full-cycle AI systems, guiding components from data collection through preprocessing, model inference, and structured output delivery. Develop and refine Retrieval-Augmented Generation (RAG) pipelines and autonomous agent workflows using modern frameworks such as LangChain, LangGraph, and ADK.
Deploy and manage machine learning models in cloud environments—across AWS, GCP, or Azure—using containerization with Docker and orchestration via Kubernetes to ensure scalability and uptime. Establish feedback mechanisms to assess model behavior, including accuracy, relevance, and hallucination rates, and implement monitoring solutions to detect performance drift over time.
Collaborate with engineering teams to embed AI capabilities into core product features and partner with product managers to define functional requirements. Fine-tune open-source language models like Llama and Mistral for domain-specific tasks, optimizing them for low latency and cost efficiency.
Requirements
- Minimum of five years of professional software engineering experience
- Strong coding skills in Python or other relevant languages such as Java, Scala, or JavaScript
- Deep understanding of Transformer-based architectures, embedding techniques, and vector similarity search methods
- Proven experience integrating with major LLM APIs including OpenAI, Anthropic, and Google Vertex AI
- Hands-on work with agent frameworks such as LangChain and LangGraph
- Familiarity with vector databases and both SQL and NoSQL data stores
- Experience operating in cloud environments, particularly AWS or GCP
- Knowledge of AI patterns including RAG, few-shot prompting, and fine-tuning strategies
- Solid foundation in software engineering practices, including Git version control and CI/CD pipelines
Preferred Qualifications
- Direct experience with Google Cloud Platform services, especially Vertex AI, Firestore, and Cloud Functions
- Understanding of advanced prompt engineering methods such as Chain-of-Thought, ReAct, and Tree of Thoughts
- Background in building agentic systems where AI agents can invoke tools or APIs independently
Benefits
- Remote-first culture with a stipend to customize your home workspace
- Healthcare coverage tailored to your local region
- Flexible paid time off starting at three weeks per year, in addition to public holidays
- No internal meetings on Fridays to support focused, deep work
- Annual professional development budget for courses, certifications, or skill-building
- Monthly wellness allowance for mental and physical health support
- Yearly in-person company gatherings to strengthen team connections
- Competitive compensation reflecting your experience and impact
- Profit-sharing program to align success with company growth
- Paid sabbatical opportunity after several years of service
