Senior AI FullStack/ML Engineer | Python, Golang, FastAPI

AI/ML Full-Stack Engineer We are seeking an experienced AI/ML Full-Stack Engineer who can bridge the gap between machine learning theory and production software engineering. This position is ideal for an engineer who understands more than simply consuming AI APIs. You should be comfortable understanding how machine learning systems work internally, reasoning about the mathematics behind them, and turning those ideas into reliable, scalable software. You'll work across AI/ML systems and backend infrastructure, using technologies such as Python, Go, FastAPI, databases, APIs, and modern machine learning frameworks. What You'll Be Working On In this role, you will design and build software that incorporates machine learning and artificial intelligence into real-world products. Your responsibilities will include: Building backend services and AI-powered applications using Python and Go Designing scalable APIs and services with FastAPI Integrating machine learning models into production applications Working with LLMs, generative AI, and other ML technologies where appropriate Designing data flows between models, APIs, databases, and external systems Evaluating model quality, accuracy, performance, latency, and other engineering trade-offs Debugging and improving existing AI and backend systems Applying statistical and mathematical reasoning when designing ML solutions Developing reliable services that can scale beyond prototypes and proof-of-concepts Making architectural decisions and independently solving complex technical challenges Learning and evaluating new AI/ML tools, frameworks, and models as the technology evolves What We're Looking For We need someone with strong software engineering skills who also has a genuine understanding of machine learning and artificial intelligence. You should have: Significant hands-on experience developing software with Python Practical experience building applications or services with Go Strong experience designing and developing APIs with FastAPI A solid understanding of machine learning fundamentals and common ML algorithms Knowledge of the mathematics used in AI and ML, including: Probability Statistics Linear algebra Optimization Experience implementing, deploying, or integrating machine learning models Strong backend engineering fundamentals Experience working with databases, REST APIs, and distributed systems A good understanding of data structures and scalable software architecture The ability to take a complex technical problem from concept through implementation Strong debugging and analytical problem-solving skills Experience writing clean, maintainable, production-quality code Technologies and Experience That Would Be a Strong Advantage Experience in any of the following areas would be highly valuable: Large Language Models and Generative AI Deep learning architectures Training and deploying machine learning models Model inference and optimization Fine-tuning machine learning models PyTorch, TensorFlow, scikit-learn, or related frameworks Retrieval-Augmented Generation (RAG) Vector search and vector databases AI agents and tool/function calling Docker and container-based deployments AWS, Google Cloud, or Microsoft Azure Microservices and distributed architectures Data engineering workflows ML pipelines and model evaluation systems The Type of Engineer Who Will Succeed Here This role requires someone who can move comfortably between theory and implementation. For example, you should be able to discuss why a machine learning approach might work, understand the statistical or mathematical reasoning behind it, evaluate its limitations, and then translate that understanding into production code. Simply knowing how to send prompts to an LLM API is not sufficient for this role. We are looking for an engineer who understands the underlying technology and can independently design and build the surrounding systems required to make AI/ML solutions useful, reliable, and scalable. When You Apply Please tell us about an AI or machine learning application you have personally built. In your response, please explain: What problem the application solved What parts of the system you personally designed and implemented The architecture and technologies you used Your experience using Python, Go, and FastAPI How machine learning concepts, algorithms, statistics, or mathematical reasoning influenced your solution Any technical challenges or trade-offs you encountered while building it We are particularly interested in understanding your personal contribution and technical reasoning, not just the technologies you have worked with.