AI Engineer

About the Role

We 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 Responsibilities

• Rapid 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 & Skills

• Proven 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 Offer

• Competitive 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.