AI Engineer

About the RoleWe are an innovative technology company building next-generation AI products. We move fast, value engineering excellence, and prioritize shipping real-world solutions over endless research.We are seeking a highly skilled AI Engineer who specializes in bridging the gap between applied research and live engineering. While many can build a local notebook demo, we need an engineer who can take a rough Proof of Concept (PoC) and architect, optimize, and deploy it into a highly scalable, robust production environment. You will own the entire lifecycle of our AI applications, ensuring that what works in a prototype works reliably for thousands of users.Key ResponsibilitiesRapid Prototyping: Partner with product teams to build functional PoCs using the latest foundational models, custom ML architectures, and agentic workflows to validate business use cases.Production Engineering: Rewrite, refactor, and harden exploratory PoC code into scalable, modular, and heavily tested production software.Model Deployment & Serving: Containerize models and deploy them using modern serving frameworks (such as vLLM, Triton, or Ray Serve) to ensure high throughput and low-latency inference.Cost & Performance Optimization: Implement techniques like caching, request batching, and model quantization to keep inference costs sustainable without degrading the user experience.MLOps & Observability: Build the infrastructure to monitor live model performance, track data drift, manage model versioning, and implement automated retraining or fallback pipelines.Required Experience & SkillsProven Track Record: Demonstrated experience taking at least one major AI/ML project from an initial Proof of Concept all the way to a live, scalable production environment.Software Engineering Excellence: Expert-level Python programming skills. You write clean, testable, production-grade code and understand system design, API architecture (REST/gRPC), and distributed systems.AI/ML Frameworks: Deep practical experience with PyTorch, along with modern GenAI tooling and ecosystems (Hugging Face, LangChain, LlamaIndex).Infrastructure Mastery: Hands-on experience with cloud platforms (AWS, GCP, or Azure), Docker, Kubernetes, and continuous integration/continuous deployment (CI/CD) pipelines.Database Fundamentals: Strong working knowledge of SQL, NoSQL, and vector databases (such as Pinecone, Weaviate, or Milvus) for RAG architectures.What We OfferCompetitive base salary and equity package.Comprehensive health, dental, and vision insurance.Fully remote work environment with a flexible schedule.The opportunity to build AI products that are actively shipped to and used by real customers, rather than sitting in a research repository.